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107 results for “Camera trap data”
Mammal occurrence data derived from camera traps in grassland-shrubland ecotones at 24 sites in the Jornada Basin, southern New Mexico, USA, 2014-ongoing
The objective of this ongoing study is to investigate how abundance, distribution, and activity of mammals (>= 1 kg) vary across grassland to shrubland ecotones in the northern Chihuahuan Desert. This dataset includes animal occurrence data derived from camera trap images captured in 24 grassland-to-shrubland ecotone sites in the Jornada Basin, Dona Ana County, New Mexico, USA. The data set contains occurrence records from 14 mammal species with the date and time a species was detected. Also included are the number of individuals in a photo, operational dates and number of functional camera days for cameras, total number of trap nights a camera was active, and geographical coordinates of camera trap locations. Sampling is ongoing and occurs during the monsoon season from July-November. Sampling has occurred annually since 2014.
Underwater Camera Trap Photo Data
<p>Repository for underwater UV camera trap data, collected during Summer 2021 at Driftwood Park, Admiralty Bay. Photos focus on octopus den locations and octopus behavior but capture regular conspecific and interspecific interactions. </p>
Supporting data for "Estimating animal density for a community of species using information obtained only from camera-traps"
<p>Data underlying a paper published in Methods in Ecology and Evolution (<a href="https://doi.org/10.1111/2041-210X.13930">https://doi.org/10.1111/2041-210X.13930</a>).</p> <p>These data are suitable for estimating animal density using the Random Encounter Model and include: i) detection counts for 35 species across 510 camera-trap locations; ii) movement speeds (estimated by tracking animal movements in camera-trap image sequences), iii) activity times (filtered so that records of the same species at the same location are > 60 minutes apart), and iv) measurements of the angular and radial distance from camera-traps for animals that were detected.</p>
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>
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>
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>
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>
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>
Рис. 5. Пятнистый оΛень неоΑнократно снят фотоΛовушками в бассейнах рек Обор и Àурмин. 15.10.2020. 14.49. Фото А. С. БатаΛова Fig. 5. Sika deer repeatedly photographed by camera traps in the basins of the rivers Obor and Durmin; 15.10.2020. 14:49. Photo by A. S. Batalova in New data on the distribution of sika deer Cervus nippon Temminck, 1838 in the Lower Amur Region
Рис. 5. Пятнистый оΛень неоΑнократно снят фотоΛовушками в бассейнах рек Обор и Àурмин. 15.10.2020. 14.49. Фото А. С. БатаΛова Fig. 5. Sika deer repeatedly photographed by camera traps in the basins of the rivers Obor and Durmin; 15.10.2020. 14:49. Photo by A. S. Batalova
Рис. 1. Карта-схема заповеΔника «БоΛьшехехцирский» и распоΛожение фотоΛовушек на территории. ЛегенΔа: спΛошная черная Λиния — границы заповеΔника; пунктирная Λиния — границы заказника «Хехцирский»; красный кружок — место установки фотоΛовушки Fig. 1. The map of the Bolshekhekhtsirsky State Nature Reserve and the location of camera traps. Legend: solid black line boundaries of the reserve; dotted line — bou in New data on the mammalian fauna of the Bolshekhekhtsirsky Nature Reserve
Рис. 1. Карта-схема заповеΔника «БоΛьшехехцирский» и распоΛожение фотоΛовушек на территории. ЛегенΔа: спΛошная черная Λиния — границы заповеΔника; пунктирная Λиния — границы заказника «Хехцирский»; красный кружок — место установки фотоΛовушки Fig. 1. The map of the Bolshekhekhtsirsky State Nature Reserve and the location of camera traps. Legend: solid black line boundaries of the reserve; dotted line — bou
Supporting data for "Snap happy: camera traps are an effective sampling tool when compared to alternative methods"
<p>Author recommendations and response ratios extracted from studies comparing camera traps to another survey method. These data underlie the analyses in a the journal article 'Snap happy: camera traps are an effective sampling tool when compared to alternative methods', published in the journal Royal Society Open Science (https://doi.org/10.1098/rsos.181748). </p>
Fig. 4 in Conservation importance of Nakai-Nam Theun National Protected Area, Laos, for small carnivores based on camera trap data
Fig. 4. Distribution of camera-trap records for Mustelidea and Herpestidea in Nakai-Nam Theun NPA from 2006–2011.
Fig. 3 in Conservation importance of Nakai-Nam Theun National Protected Area, Laos, for small carnivores based on camera trap data
Fig. 3. Distribution of camera-trap records for Viverridae and linsang in Nakai-Nam Theun NPA from 2006–2011.
Fig. 2 in Conservation importance of Nakai-Nam Theun National Protected Area, Laos, for small carnivores based on camera trap data
Fig. 2. Total survey effort (in camera trap days) per month over the 2006–2011 survey period (bars) and relative species encounter rate (i.e., total independent photos of small carnivore spp./total camera trap day for the month). Relative encounter rates were highest during the warmest and well-surveyed months—peaks are observed in March (start of the warm season) and October (still within the warm season). Despite high survey effort in January and December (cold season), encounter rates were low.
Fig. 1 in Conservation importance of Nakai-Nam Theun National Protected Area, Laos, for small carnivores based on camera trap data
Fig. 1. Camera-trap sampling effort within Nakai–Nam Theun NPA in 2006–2011 (c.f. Table 1; Johnson et al., 2007) at 10 survey blocks; in chronological order of survey: (1) Khamkeut – Nam San; (2) Nam On – Boualapha; (3) Nam On – Gnomalath; (4) Khamkeut – Thong Pae; (5) Nam Chae – Makfuang; (6) Nam Chae – Navang; (7) Phou Vang – Houay Nam Heuy; (8) Thong Xet; (9) Nam Mon – Thongkacheng; (10) Nam Theun – reservoir.
Fig. 1. Camera trap data was collected from 14 in Terrestrial Activity Patterns Of Wild Cats From Camera-Trapping
Fig. 1. Camera trap data was collected from 14 protected areas within Thailand. NP = national park; WS = wildlife sanctuary; NH = non-hunting area.
Data from: Holistic monitoring of aquatic and terrestrial vertebrates by camera trapping and aquatic environmental DNA
Open the record for dataset details and reuse information.
Camera trap data suggest uneven predation risk across vegetation types in a mixed farmland landscape
Open the record for dataset details and reuse information.
Data from: Continuous-time spatially explicit capture-recapture models, with an application to a jaguar camera-trap survey
Open the record for dataset details and reuse information.
Locations of black bear (Ursus americanus) reproduction in Nevada from camera-trap data
<p>Understanding factors creating species range boundaries is a fundamental goal of ecology and biogeography. American black bears recolonized the western Great Basin from the Sierra Nevada in the late 1900s but this expansion has not proceeded further into the Great Basin despite the presence of suitable habitat. We deployed 100 camera traps across the occupied range of black bears in the U.S. state of Nevada and tracked bear detections across 3 years. A scent lure was applied in camera trap viewsheds to increase bear detections. We classified detections of bear cubs separately from detections of only adult bears, to serve as an indicator of black bear reproduction occurring at sites. Data are provided in the format necessary for a analysis with multistate occupancy model. Analysis of these data revealed low incidence of reproduction at the periphery of black bear range in the western Great Basin, which likely contributes to range boundary formation.</p>
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International Brain Laboratory public data
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