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1,832 results for “Cameras”
High speed camera video files for analyzing the cracking susceptibility of AA6005 alloy
<p>The paper based on this data was published in the CIRP/Photonics LANE 2022 conference at Furth, Germany. The high speed camera video raw data for future reference on solidification cracking susceptibility of AA 6005 alloy using the Digital Image correlation technique.</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>
VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction
<p>This dataset includes all the data and trained models to replicate our work for VGQ-CNN (accepted for IJCNN 2022). You can find the code to use this dataset on <a href="https://github.com/AuCoRoboticsMU/vgq-cnn">github</a>. To replicate the work done for VGQ-CNN, use the data in vgq-dset.zip. Trained models of VGQ-CNN, Fast-VGQ-CNN and GQ-CNN are available in VGQ-CNN_models.zip.</p> <p> </p> <p>To create your own, subsampled training and testing data, adjust our code on github to your subsampling constraints and use full_rendered_dset (created by unpacking full_rendered_dset_tensors.zip and full_rendered_dset_images.zip into the unpacked directory of full_rendered_dset_info.zip).</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>
Sprites observed with Global Meteor Network camera DE000C on 2022-06-30
<p>This dataset contains compressed video observations of sprites, made with one low-light video camera of the Global Meteor Network. The camera, DE000C, is located in Sörup, Northern Germany, and has pointing azimuth 217˚ (so South-West), elevation 39˚.</p> <p>The files are 10.24 second videos compressed in the four-frame format. They are just FITS files with four frames, containing per pixel 1) the maximum value over 256 frames 2) the frame nr (between 0 and 255) where the maximum occurred 3) the mean value of all 256 frames and 4) the RMS of the 256 values.</p> <p>The platepar files contain calibration data that can be read with the sofware at https://github.com/CroatianMeteorNetwork/RMS/. The astrometry contained in the fits files is derived from this and may be less accurate.</p> <p>A stack of the maxpixel images is also contained.</p>
Light camera position detection - experiment data
<p>Experiment data measure the accuracy of automatic light fixtures position detection using a single monocular web camera with different resolutions.</p>
CNN for the classification of ICE-CAMERA images of Antarctic ice particles
<p>-The file 'ZENODO_FILES.rar' contains the GoogleNet Convolutional Neural Network (CNN) trained to classify pre-processed ICE-CAMERA images (224*224*3) into 14 classes. CNN was developed for (Mathworks) MATLAB® R2020b.</p> <p>-The ICE-CAMERA images used for training, validation and testing the CNN are also contained in specific folders.</p> <p> </p>
CZI (Carl Zeiss Image) dataset with artificial test camera images with various dimension for testing libraries reading
<p>Set of CZI test images created by using a simulated microscope with a test grayscale camera (no LSM or AiryScan or RGB). The filename indicates the used dimension(s) for the acquisition experiment. The files can be used to test the basic functionality of libraries reading CZI files.</p> <p>Examples:</p> <ul> <li>S=2_T=3_CH=1.czi = 2 Scenes, 3 TimePoints and 1 Channel <ul> <li>Z-Stack <strong>was not</strong> activated inside acquisition experiment</li> </ul> </li> <li>S=2_T=3_Z=5_CH=2.czi = 2 Scenes, 3 TimePoints, 5-Z-Planes and 1 Channels <ul> <li>Z-Stack <strong>was </strong>activated inside acquisition experiment</li> </ul> </li> </ul> <p>The test files (so far) contain not any data with more "advanced" dimensions like AiryScan rawdata, illumination angles etc. Also no CZI files with pixel type RGB are included yet.</p> <p> </p> <p> </p> <p> </p>
Data belonging to the article: Estimating pre-harvest density, adult sex ratio and fecundity of white-tailed deer using wildlife cameras
<p>Adult sex ratio and fecundity (juveniles per female) are key population parameters in sustainable wildlife management, but inferring these requires abundance estimates of at least three age/sex classes of the population (male and female adults and juveniles). Prior to harvest, we used an array of 36 wildlife camera traps during 2 and 3 weeks in the early autumn of 2016 and 2017 respectively. We recorded white-tailed deer adult males, adult females and fawns from the pictures. Simultaneously, we collected fecal DNA (fDNA) from 92 20mx20m plots placed in 23 clusters of four plots between the camera traps. We identified individuals from fDNA samples with microsatellite markers and estimated the total sex ratio and population density using Spatial Capture Recapture (SCR). The fDNA-SCR analysis concluded equal sex ratio in the first year and female bias in the second year, and no difference in space use between sexes (fawns and adults combined). Camera information was analyzed in a Spatial Capture (SC) framework assuming an informative prior for animals' space use, either (1) as estimated by fDNA-SCR (same for all age/sex classes), (2) as assumed from the literature (space use of adult males larger than adult females and fawns), (3) by inferring adult male space use from individually-identified males from the camera pictures. These various SC approaches produced plausible inferences on fecundity, but also inferred total density to be lower than the estimate provided by fDNA-SCR in one of the study years. SC approaches where adult male and female were allowed to differ in their space use suggested the population had a female-biased adult sex ratio. In conclusion, SC approaches allowed estimating the pre-harvest population parameters of interest and provided conservative density estimates.</p>
Text-fig. 5. Extant Fraxinus fruits photographed by digital camera and X-ray microscopy. a: F. excelsior L. (Sect. Fraxinus); b: F. xanthoxyloides WALL. (Sect. Sciadanthus); c: F. malacophylla HEMSL. (Sect. Ornus). Red arrow refers to the air sac above seed, scale bars = 1 cm. in Fraxinus L. (Oleaceae) Fruits From The Early Oligocene Of Southwest China And Their Biogeographic Implications
Text-fig. 5. Extant Fraxinus fruits photographed by digital camera and X-ray microscopy. a: F. excelsior L. (Sect. Fraxinus); b: F. xanthoxyloides WALL. (Sect. Sciadanthus); c: F. malacophylla HEMSL. (Sect. Ornus). Red arrow refers to the air sac above seed, scale bars = 1 cm.
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>
Jupiter Notebook and example data for "Prospects for a camera-based detector for Neutron Reflectometry"
<p>We report the outcome of a proof-of-principle (IPTS-29165) neutron reflectivity measurement obtained using a neutron scintillator and a Photonis (brand) camera. We were motivated to test this technology because it provides much better spatial resolution and count rate capability than the BL4A <sup>3</sup>He position sensitive detector (Table 1). The report describes the detector setup, challenges encountered, a reflectivity measurement and next steps.</p> <p>Two example measurements are provided and a Jupyter Notebook to create a NumPy binary file consisting of event positions and times.</p>
Multi-Camera Action Dataset (MCAD)
<p>Action recognition has received increasing attentions from the computer vision and machine learning community in the last decades. Ever since then, the recognition task has evolved from single view recording under controlled laboratory environment to unconstrained environment (i.e., surveillance environment or user generated videos). Furthermore, recent work focused on other aspect of action recognition problem, such as cross-view classification, cross domain learning, multi-modality learning, and action localization. Despite the large variations of studies, we observed limited works that explore the open-set and open-view classification problem, which is a genuine inherited properties in action recognition problem. In other words, a well designed algorithm should robustly identify an unfamiliar action as “unknown” and achieved similar performance across sensors with similar field of view. The Multi-Camera Action Dataset (MCAD) is designed to evaluate the open-view classification problem under surveillance environment.</p> <p>In our multi-camera action dataset, different from common action datasets we use a total of five cameras, which can be divided into two types of cameras (StaticandPTZ), to record actions. Particularly, there are three Static cameras (Cam04 & Cam05 & Cam06) with fish eye effect and two PanTilt-Zoom (PTZ) cameras (PTZ04 & PTZ06). Static camera has a resolution of 1280×960 pixels, while PTZ camera has a resolution of 704×576 pixels and a smaller field of view than Static camera. What’s more, we don’t control the illumination environment. We even set two contrasting conditions (Daytime and Nighttime environment) which makes our dataset more challenge than many controlled datasets with strongly controlled illumination environment.The distribution of the cameras is shown in the picture on the right.</p> <p>We identified 18 units single person daily actions with/without object which are inherited from the KTH, IXMAS, and TRECIVD datasets etc. The list and the definition of actions are shown in the table. These actions can also be divided into 4 types actions. Micro action without object (action ID of 01, 02 ,05) and with object (action ID of 10, 11, 12 ,13). Intense action with object (action ID of 03, 04 ,06, 07, 08, 09) and with object (action ID of 14, 15, 16, 17, 18). We recruited a total of 20 human subjects. Each candidate repeats 8 times (4 times during the day and 4 times in the evening) of each action under one camera. In the recording process, we use five cameras to record each action sample separately. During recording stage we just tell candidates the action name then they could perform the action freely with their own habit, only if they do the action in the field of view of the current camera. This can make our dataset much closer to reality. As a results there is high intra action class variation among different action samples as shown in picture of action samples.</p> <p>URL: http://mmas.comp.nus.edu.sg/MCAD/MCAD.html</p> <p><strong>Resources:</strong></p> <ul> <li><strong>IDXXXX.mp4.tar.gz</strong> contains video data for each individual</li> <li><strong>boundingbox.tar.gz</strong> contains person bounding box for all videos</li> <li><strong>protocol.json</strong> contains the evaluation protocol</li> <li><strong>img_list.txt</strong> contains the download URLs for the images version of the video data</li> <li><strong>idt_list.txt</strong> contians the download URLs for the improved Dense Trajectory feature</li> <li><strong>stip_list.txt</strong> contians the download URLs for the STIP feature</li> </ul> <ul> <li>Manual annotated 2D joints for selected camera view and action class (available via http://zju-capg.org/heightmap/)</li> </ul> <p><strong>How to Cite:</strong></p> <p>Please cite the following paper if you use the MCAD dataset in your work (papers, articles, reports, books, software, etc):</p> <ul> <li>Wenhui Liu, Yongkang Wong, An-An Liu, Yang Li, Yu-Ting Su, Mohan Kankanhalli<br> <strong>Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking</strong><br> <em>IEEE Winter Conference on Applications of Computer Vision (WACV)</em>, 2017.<br> http://doi.org/10.1109/WACV.2017.28</li> </ul>
Automatic camera images of Suldenferner 20150821 to 20150913
<p>Photographs taken by an automatic camera overlooking the debris-covered part of Suldenferner, Ortler Group, Italy.</p> <ul> <li>Format: JPEG, provided in .zip format</li> <li>Filenames: numerical (0015 to 1502)</li> <li>Period covered: 21 August - 13th September 2015</li> <li>Image interval: daily 03:27 then 15 minute increments until 18:42, timestamp in UTC</li> <li>Camera model: Harbortronics Timelapse package F (https://www.harbortronics.com/Products/TimeLapsePackageF/)</li> <li>Camera location: 46°29'27.7"N / 10°35'53.7" E</li> </ul> <p>Timelapse video compiled from these photos in .mp4 format.</p> <p> </p>
Phenological time lapse images from landscape camera MC112 in Parkano Mixed stand
<p>This record contains phenological time lapse images from camera Parkano Mixed stand. Camera was mounted at landscape view level at location 62.028368; 23.041569(N;E, WGS84).</p> <p>First set of images were taken between 07.05.2015--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record.<br> Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist.<br> More details on the camera installations and operation history can be found at doi 10.5281/zenodo.777952<br> The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi.<br> For further information contact Mikko Peltoniemi (mikko.peltoniemi@luke.fi)</p>
Vallée de la Sionne Snow Avalanche n. 20213009: High-speed camera recording and derived variables
<p>This repository hosts data obtained from high-speed camera measurements conducted within a large powder snow avalanche (No. 20213009) that occurred naturally at the Vallée de la Sionne test site in Switzerland. Positioned 14 meters above the ground on a vertical pylon, the high-speed camera captures visualizations of snow particles within the aerial layers. These images reveal diverse particle clusters, identifiable as bright spots due to their higher light reflectance compared to the surrounding air-snow crystal mixture.</p> <p>Contained within this repository is an overview video recording along with corresponding data on the average brightness of each image captured by the high-speed camera. This dataset facilitates the reconstruction of the temporal evolution and frequency of particle clustering, with brightness intensity acting as a proxy for mass transport. The average brightness for each image is computed from the averaging of values from 2048 x 2048 pixels (greyscale 0 to 255). These datasets complement the findings presented in the following publication:</p> <p>B. Sovilla, E. Marchetti, M. Kyburz, A. Koehler, P. Huguenin, I. Calic, M.J. Kohler, E. Surinach, and C. Pérez-Guillén, under review. "The dominant source mechanism of infrasound generation in powder snow avalanches," submitted to Geophysical Research Letters.</p>
Simulation Data & R scripts for: "Introducing recurrent events analyses to assess species interactions based on camera trap data: a comparison with time-to-first-event approaches"
<p><strong>Files descriptions:</strong></p> <p>All csv files refer to results from the different models (PAMM, AARs, Linear models, MRPPs) on each iteration of the simulation. One row being one iteration. <br>"results_perfect_detection.csv" refers to the results from the first simulation part with all the observations.<br>"results_imperfect_detection.csv" refers to the results from the first simulation part with randomly thinned observations to mimick imperfect detection.</p> <p>ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).<br>PAMM30: p-value of the PAMM running on the 30-days survey.<br>PAMM7: p-value of the PAMM running on the 7-days survey.<br>AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.<br>AAR2: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.<br>Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).<br>Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).<br>Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).<br>MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).<br>MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br>MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br> </p> <p>"results_int_dir_perf_det.csv" refers to the results from the second simulation part, with all the observations.<br>"results_int_dir_imperf_det.csv" refers to the results from the second simulation part, with randomly thinned observations to mimick imperfect detection.<br>ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).<br>p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of A on B.<br>p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of B on A.<br>AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.<br>AAR2_BAB: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.<br>AAR2_ABA: ratio value for the Avoidance-Attraction-Ratio calculating ABA/AA.<br>Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).<br>Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).<br>Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).<br>MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).<br>MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br>MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br> </p> <p><strong>Scripts files description:</strong><br>1_Functions: R script containing the functions:<br> - MRPP from Karanth et al. (2017) adapted here for time efficiency.<br> - MRPP from Murphy et al. (2021) adapted here for time efficiency.<br> - Version of the ct_to_recurrent() function from the recurrent package adapted to process parallized on the simulation datasets.<br> - The simulation() function used to simulate two species observations with reciprocal effect on each other.<br>2_Simulations: R script containing the parameters definitions for all iterations (for the two parts of the simulations), the simulation paralellization and the random thinning mimicking imperfect detection.<br>3_Approaches comparison: R script containing the fit of the different models tested on the simulated data.<br>3_1_Real data comparison: R script containing the fit of the different models tested on the real data example from Murphy et al. 2021.<br>4_Graphs: R script containing the code for plotting results from the simulation part and appendices.<br>5_1_Appendix - Check for similarity between codes for Karanth et al 2017 method: R script containing Karanth et al. (2017) and Murphy et al. (2021) codes lines and the adapted version for time-efficiency matter and a comparison to verify similarity of results.<br>5_2_Appendix - Multi-response procedure permutation difference: R script containing R code to test for difference of the MRPPs approaches according to the species on which permutation are done.</p>
Spatial behavior and diet data for discrete-choice analyses: data observed and classified from GPS video camera collars worn by female members of the Fortymile Caribou Herd across Alaska, USA, and Yukon, Canada
<p>Competition for resources and space can drive forage selection of large herbivores from the bite through the landscape scale. Animal behavior and foraging patterns are also influenced by abiotic and biotic factors. Fine-scale mechanisms of density-dependent foraging at the bite scale are likely consistent with density-dependent behavioral patterns observed at broader scales, but few studies have directly tested this assertion. Here, we tested if space use intensity, a proxy of spatiotemporal density, affects foraging mechanisms at fine spatial scales similarly to density-dependent effects observed at broader scales in caribou. We specifically assessed how behavioral choices are affected by space use intensity and environmental processes using behavioral state and forage selection data from caribou (<i>Rangifer tarandus granti</i>) observed from GPS video-camera collars using a multivariate discrete-choice modeling framework. We found that the probability of eating shrubs increased with increasing caribou space use intensity and cover of <i>Salix</i> spp. shrubs, whereas the probability of eating lichen decreased. Insects also affected fine-scale foraging behavior by reducing the overall probability of eating. Strong eastward winds mitigated the negative effects of insects and resulted in higher probabilities of eating lichen. Lastly, caribou exhibited foraging functional responses wherein their probability of selecting each food type increased as the availability (% cover) of that food increased. Space use intensity signals of fine-scale foraging were consistent with density-dependent responses observed at larger scales and with recent evidence suggesting declining reproductive rates in the same caribou population. Our results highlight the potential risks of overgrazing on sensitive forage species such as lichen. Remote investigation of the functional responses of foraging behaviors provides exciting future applications where spatial models can identify high-quality habitats for conservation.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.