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1,832 results for “Cameras”
Camera trap evidence of infant corpse carrying in wild unhabituated chimpanzees
<p>Camera trap evidence of infant corpse carrying (ICC). Folder names correspond to case IDs. Please see publication by Bersacola et al for further information. Corresponding author: Elena Bersacola (e.bersacola@exeter.ac.uk). </p> <p>Data contributors: Elena Bersacola, Américo Sanhá, Maimuna Jaló, Joana Bessa, Marina Ramon, Kimberley Hockings (ICC_CC_1; ICC_CC_2; ICC_LA_1; ICC_CGH_1); Henry Camara, Gnan Namy, Laura van Holstein, Maegan Fitzgerald, Kathelijne Koops (ICC_TB_1; ICC_TB_2; ICC_TB_3); Matthew McLennan, Vicent Kiiza, Nicholas Mpanga (ICC_KB_1); Vicky Oelze, Fiona Stewart (ICC_IV_1; ICC_IV_2)</p>
Figure 3 in Recording potential predators of herpetofauna in southern Mexico using camera traps and realistic models
Figure 3. Times of aggression events towards the frog and snake models.
Figure 2 in Insights into surveying pangolins using ground and arboreal camera traps
Figure 2: Diagram showing the relationship between camera height and camera zone.
Figure 1 in Insights into marking behavior of giant anteaters: a camera trap study in the Rupununi savannahs, Guyana
Figure 1: A map showing the location of the study site and the 51 camera trap sites.
Table ¹: Data by year and by month, for the number of cameras, sampling days, sampling effort, and relative abundance index (RAI) of records for females and cubs. in Reproductive aspects of female Andean bears (Tremarctos ornatus) in the Chingaza massif, eastern range of the Colombian Andes
<p><b>Table ¹:</b> Data by year and by month, for the number of cameras, sampling days, sampling effort, and relative abundance index (RAI) of records for females and cubs.</p><table><tbody><tr><th><b>Year</b></th><th><b>Camera traps used</b></th><th></th><th></th><th><b>Sampling days per month (sampling effort per month)</b></th><th></th><th></th><th><b>Sampling days</b></th><th><b>Camera traps</b></th><th><b>Sampling effort</b></th></tr></tbody><tbody><tr><th><b>Reconyx Wildview</b></th><td><b>Bushnell</b></td><td><b>]</b></td><td><b>F</b></td><td><b>M</b></td><td><b>A</b></td><td><b>M</b></td><td><b>]</b></td><td><b>]</b></td><td><b>A</b></td><td><b>S</b></td><td><b>O</b></td><td><b>N</b></td><td><b>D</b></td><td><b>per year</b></td><td><b>per year</b></td><td><b>per year</b></td></tr><tr><th>2011</th><td>2</td><td>4</td><td>0</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>3 (18)</td><td>30</td><td>30</td><td>30</td><td>93</td><td>6</td><td>558</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(180)</td><td>(180)</td><td>(180)</td><td></td><td></td><td></td></tr><tr><th>2012a</th><td>2</td><td>4</td><td>0</td><td>31</td><td>29</td><td>31</td><td>30</td><td>31</td><td>30</td><td>31</td><td>31</td><td>30</td><td>31</td><td>30</td><td>30</td><td>365</td><td>6</td><td>2190</td></tr><tr><th></th><td></td><td></td><td></td><td>(186)</td><td>(174)</td><td>(186)</td><td>(180)</td><td>(186)</td><td>(180)</td><td>(186)</td><td>(186)</td><td>(180)</td><td>(186)</td><td>(180)</td><td>(180)</td><td></td><td></td><td></td></tr><tr><th>2012b</th><td>0</td><td>0</td><td>12</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>30</td><td>30</td><td>60</td><td>12</td><td>720</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(360)</td><td>(360)</td><td></td><td></td><td></td></tr><tr><th>2013</th><td>2</td><td>4</td><td>14</td><td>31</td><td>28</td><td>31</td><td>30</td><td>31</td><td>30</td><td>31</td><td>31</td><td>30</td><td>30</td><td></td><td></td><td>303</td><td>20</td><td>6060</td></tr><tr><th></th><td></td><td></td><td></td><td>(620)</td><td>(560)</td><td>(620)</td><td>(600)</td><td>(620)</td><td>(600)</td><td>(620)</td><td>(620)</td><td>(600)</td><td>(600)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>2014</th><td>0</td><td>2</td><td>9</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>3 (33)</td><td>30</td><td>33</td><td>11</td><td>363</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(330)</td><td></td><td></td><td></td></tr><tr><th>2015a</th><td>0</td><td>2</td><td>9</td><td>31</td><td>28</td><td>31</td><td>30</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>120</td><td>11</td><td>1320</td></tr><tr><th></th><td></td><td></td><td></td><td>(341)</td><td>(308)</td><td>(341)</td><td>(330)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>2015b</th><td>0</td><td>0</td><td>117</td><td></td><td></td><td></td><td></td><td>2 (234)</td><td>30</td><td>31</td><td>31</td><td>30</td><td>31</td><td>30</td><td>30</td><td>215</td><td>117</td><td>25,155</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(3510)</td><td>(3627)</td><td>(3627)</td><td>(3510)</td><td>(3627)</td><td>(3510)</td><td>(3510)</td><td></td><td></td><td></td></tr><tr><th>2016</th><td>0</td><td>0</td><td>117</td><td>31</td><td>29</td><td>31</td><td>30</td><td>30</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>151</td><td>117</td><td>17,667</td></tr><tr><th></th><td></td><td></td><td></td><td>(3627)</td><td>(3393)</td><td>(3627)</td><td>(3510)</td><td>(3510)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Sum of sampling effort per month over</th><td>4774</td><td>4435</td><td>4774</td><td>4620</td><td>4550</td><td>4290</td><td>4433</td><td>4433</td><td>4308</td><td>4593</td><td>4263</td><td>4560</td><td>Total sampling effort</td><td>54,033</td></tr><tr><th>the years</th></tr><tr><th>Records of females with cubs (4 <b>–</b></th><td>1</td><td>2</td><td>1</td><td>3</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td><td>4</td><td></td><td></td><td></td></tr><tr><th>7 months)</th></tr><tr><th>RAI of females with cubs (4 <b>–</b> 7 months)</th><td>0.21</td><td>0.45</td><td>0.21</td><td>0.65</td><td>0.22</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.44</td><td>0.00</td><td>0.88</td><td></td><td></td><td></td></tr><tr><th>Records of estimated births</th><td></td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>7</td><td>3</td><td>2</td><td>0</td><td>3</td><td>3</td><td>0</td><td></td><td></td><td></td></tr><tr><th>RAI of estimated births</th><td></td><td></td><td>0.21</td><td>0.00</td><td>0.21</td><td>0.00</td><td>0.00</td><td>1.63</td><td>0.68</td><td>0.45</td><td>0.00</td><td>0.65</td><td>0.70</td><td>0.00</td><td></td><td></td><td></td></tr></tbody></table>
Acoustic video cameras multi-species multi-cameras Validation Dataset (VD) for Deep Learning applications
<p>This video dataset, called also VD (Validation Dataset), is designed to test/validate, on a real-world case, deep learning models to identify fish species in sonar camers video flux. It includes data from two different type of cameras (ARIS and DIDSON), two sites (Touques and Selune rivers in Normandy, France), 6 different fishes classes (Atlantic Salmon, European Eel, Sea Lamprey, Allis Shad, European Catfish and generic unidentified fish). This dataset is composed by around 40h of videos, to test the efficiency of the models in the frame of ecological studies and to assess their real-applicability on monitoring sites data. Two sheets are given as the ground truth in which all fish passages (for fish sizes larger than 20 cm) are annotated. No bounding boxes are given.</p>
Figure 2 in Camera traps reveal use of caves by Asiatic black bears (Ursus thibetanus gedrosianus) (Mammalia: Ursidae) in southeastern Iran
Figure 2. Camera trap set up on (A) rocks and (B) tree in the Dehbakri-Dalfard area.
Fig. 1 in Assessing large mammal and bird richness from camera-trap records in the Hukaung Valley of Northern Myanmar
Fig. 1. Location of Hukaung Valley Wildlife Sanctuary and Core study area (hatched) in Northern Myanmar.
Tiwi Island cat density camera-trap data 2017 and 2018
<p>This data was collected as part of the National Environmental Science Program's Threatened Species Recovery Hub (Project 1.1.12 - Mitigating cat impacts on the brush-tailed rabbit-rat). This dataset includes all detections of feral cats recorded on large grids of camera-traps deployed at four locations on the Tiwi Islands. Each of these grids consisted of 70 camera-traps, deployed in 14 rows of five cameras, with each camera spaced ~500 m apart. Camera-traps remained continuously recording for eight weeks. The location of each camera-trap is also provided.</p>
Camera traps Red deer exhibit spatial and temporal responses to hiking activity
<p>Outdoor recreation has the potential to impact the spatial and temporal distribution of animals. We explore interactions between red deer (Cervus elaphus) and hikers along a popular hiking path in the Scottish Highlands. We placed camera traps in transects at different distances (25, 75 and 150 metres) from the path to study whether distance from hiker activity influences the number of deer detected. We compared this with the detection of red deer in an additional, spatially isolated area (one km away from any other transects and the hiking path). We collected count data on hikers at the start of the path and explored hourly (red deer detection during the day), daily, diurnal (day vs night), and monthly spatial distributions of red deer. Using Generalized Linear Mixed Models with forward model selection, we found that the distribution of deer changed with the hiking activity. We found that fewer red deer were detected during busy hourly hiking periods. We found that during the day, more red deer were detected at 150m than at 25m. Moreover, during the day, red deer were detected at a greater rate in the isolated area than around the transects close to the path and more likely to be found close to the path at night. This suggests that avoidance of hikers by red deer, in this study area, takes place over distances greater than 75m and that red deer are displaced into less disturbed areas when the hiking path is busy. Our results suggest that the impact of hikers is short-term, as deer return to the disturbed areas during the night.</p>
Ten year camera trap dataset of tigers in India
<p>1. With continued global changes, such as climate change, biodiversity loss and habitat fragmentation, the need for assessment of long-term population dynamics and population monitoring of threatened species is growing. One powerful way to estimate population size and dynamics is through capture-recapture methods. Spatial capture (SCR) models for open populations make efficient use of capture-recapture data, while being robust to design changes. Relatively few studies have implemented open SCR models and to date, very few have explored potential issues in defining these models. We develop a series of simulation studies to examine the effects of the state space definition and between-primary-period movement models on demographic parameter estimation. We demonstrate the implications on a 10-year camera-trap study of tigers in India. (This is the dataset presented here).</p> <p>2. The results of our simulation study show that movement biases survival estimates in open SCR models when little is known about between-primary-period movements of animals. The size of the state space delineation can also bias the estimates of survival in certain cases.</p> <p>3. We found that both the state space definition and between-primary-period movement specification affected survival estimates in the analysis of the tiger dataset (posterior mean estimates of survival ranged from 0.71-0.89).</p> <p>4. In general, we suggest that open SCR models can provide an efficient and flexible framework for long-term monitoring of populations; however, in many cases, realistic modeling of between-primary-period movements is crucial for unbiased estimates of survival and density.</p>
Data for: Estimation of density distribution in unmarked populations using camera traps
<p>Reliable estimates of species distribution and density are essential to ecology. Camera traps have revolutionized wildlife monitoring, and camera-trap data are increasingly used to study animal distribution and density. </p> <p>We propose a general framework and present a statistical model to estimate the distribution and density of species for which individuals lack identifying marks. Numbers recorded at traps allow spatial variation in density to be modelled, while distances of detected animals from the cameras allow correction for missed animals in the detection sector, using distance sampling.</p> <p>We test the model by simulating a camera-trap survey of a population of single animals, and we apply the model to data from a field study of Reeves's muntjac. The simulation indicated that the estimates of population density were unbiased, and the model performed well in depicting spatial variation in density. In the field study, the model estimated that the overall population density of Reeves's muntjac was 4.1 ind/km<sup>2</sup>, and mapped its density distribution across the study area.</p> <p>We provide a method to estimate unmarked species' density distribution using camera-trap data. Application of the model can help investigate the distribution and density of many ground-dwelling solitary animal populations lacking individually recognizable markings. We expect our method to provide an effective means for wildlife monitoring.</p>
Supplementary material 1 from: Thaung R, Frechette J, Luskin MS, Amir Z (2023) Combining Camera Trap Data and Environmental Data to Estimate the Effects of Environmental Gradients on Abundance of the Asian Elephant Elephas maximus in Cambodia. Biodiversity Information Science and Standards 7: e112100. https://doi.org/10.3897/biss.7.112100
Environmental Variables Used in the study
A 8-month Study on the Use of Intra-oral Camera and Text Messages on Gingivitis Control
ClinicalTrials.gov study NCT03439969. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Assessment of Prototype Hand-held Fundus Camera
ClinicalTrials.gov study NCT01244412. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Low Dose One-Day Tc99m Protocol With a High-Efficiency Cardiac Dedicated Gamma Camera For Detection of Coronary Disease
ClinicalTrials.gov study NCT01135095. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Simulation-based Training of Operating Assistants: Procedure- Versus Camera Navigation Training
ClinicalTrials.gov study NCT02530099. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Intra-oral Camera in Gingival Health
ClinicalTrials.gov study NCT02725983. IPD Sharing: NO. Countries: 0. Publications: 4.
Feasibility Study of Retinal Screening Using the RetinaVue 100 Camera in Outpatient Dialysis Centers
ClinicalTrials.gov study NCT02823600. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Measuring agreement among experts in classifying camera images of similar species
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