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36 results for “camera-trap”
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>
Wildlife in the greater Phoenix, Arizona, USA metropolitan area: results of a camera-trapping project (2019-2020)
The goal of this research project was to evaluate how wildlife populations responded to the gradient of urbanization. We deployed 50 wildlife cameras across the gradient of urbanization from downtown Phoenix to nearby wildland areas from January 2019 to August 2020. We documented a suite of wildlife species, from small mammals and birds to large mammals. Data present whether a species was detected at a site during this time period.
Wildlife along the Salt River corridor of the greater Phoenix, Arizona, USA metropolitan area: results of a camera-trapping project (2020-2021)
The goal of this research project was to evaluate how wildlife populations responded to the gradient of urbanization, water, and vegetation. We deployed 43 wildlife cameras across the gradient of urbanization January 2021 to January 2022. We documented a suite of wildlife species, from small mammals and birds to large mammals. Data present whether a species was detected at a site during this time period.
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>
Fig. 2 in Camera-Trapping Survey Of Mammals In And Around Imbak Canyon Conservation Area In Sabah, Malaysian Borneo
Fig. 2. The observed species accumulation curve (-o-) and 95% CIs (---) for mammalian species in and around Imbak Canyon Conservation Area. The curve was constructed using abundancebased rarefaction approach (i.e., by using the number of independent photographs captured) with 100 randomisation runs in EstimateS (Colwell, 2009).
Fig. 3. Activity patterns for 14 in Camera-Trapping Survey Of Mammals In And Around Imbak Canyon Conservation Area In Sabah, Malaysian Borneo
Fig. 3. Activity patterns for 14 mammal species (with n ≥ 8) photocaptured in and around Imbak Canyon Conservation Area in central Sabah, Malaysian Borneo. Dotted bar indicates percent frequency of independent photographs taken during the day time (0600–1800 hours); Black bar indicates percent frequency of independent photographs taken during night time (1800–0600 hours). Species are listed in order of decreasing frequency of diurnal activity. Numbers in parentheses indicate sample size.
Fig. 1 in Camera-Trapping Survey Of Mammals In And Around Imbak Canyon Conservation Area In Sabah, Malaysian Borneo
Fig. 1. Imbak Canyon Conservation Area (ICCA) in central Sabah, northern part of Malaysian Borneo. Circles show the localities of 13 plots (P1–P13) where camera traps were placed (+). Each plot is approximately 3.5 km in radius.
Fig. 2 in Terrestrial Activity Patterns Of Wild Cats From Camera-Trapping
Fig. 2. Density estimates of daily activity patterns of six felid species in Thailand. Solid lines are kernel-density estimates; dashed lines are trigonometric sum distributions. The short vertical lines above the x-axis indicate the times of individual photographs.
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.
Fig. 4 in Terrestrial Activity Patterns Of Wild Cats From Camera-Trapping
Fig. 4. Daily activity patterns of tigers and leopards in five study areas in Thailand. Individual photograph times are indicated by the short vertical lines above the x-axis. The overlap coefficient is the area under the minimum of the two density estimates, as indicated by the shaded area in each plot.
Fig. 3 in Terrestrial Activity Patterns Of Wild Cats From Camera-Trapping
Fig. 3. Daily activity patterns of and overlap of Asiatic golden cat compared to leopard cat and clouded leopard in Khao Yai National Park, Thailand. Individual photograph times are indicated by the short vertical lines above the x-axis. The overlap coefficient is the shaded area under the two density estimates.
Fig. 3 in Assessing large mammal and bird richness from camera-trap records in the Hukaung Valley of Northern Myanmar
Fig. 3. Trend lines, correlations and p-values for the relationship between number of camera trap nights per season per area (effort) versus number of species photographed (diversity) in the Core study area (solid line & solid circle) and at and near camera trap locations Outside the Core area (dash line & hollow circle) in the Hukaung Valley, Myanmar (season [= year] data from Naing 2015).
Fig. 2 in Assessing large mammal and bird richness from camera-trap records in the Hukaung Valley of Northern Myanmar
Fig. 2. Camera stations, and the composite areas within 3 km of each station, in the Core study area (A) and Outside of the Core area (B) in the Hukaung Valley Wildlife Sanctuary of Myanmar
Data from: Continuous-time spatially explicit capture-recapture models, with an application to a jaguar camera-trap survey
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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>
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>
Adapting camera-trap placement based on animal behaviour for rapid detection: a focus on the Endangered, white-bellied pangolin (Phataginus tricuspis)
<p>Table containing detection data of species using two camera trap placement strategies (log vs non-log)</p>
Camera-trap data for fitting the random encounter model to estimate densities of coyotes and black-tailed jackrabbits in the Mojave Desert
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Integrating temporal and spatial dimensions of alpine adaptation: Camera-trap insights on bharal (Pseudois nayaur) in Giant Panda National Park
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Large-antlered muntjac (Muntiacus vuquangensis) camera-trap photos from Virachey NP, Cambodia
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