Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
28
datasets available to search
ShareScore release 0.9.0
Dataset results
28 results for “Camera trap survey”
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>
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>
Camera trap fauna survey in Talissieu (France) 2016-2018
<p>Mammals and birds presence identified from a camera trap survey in a mixed forest and fields environment in the Lavours marsh (France). This dataset gathers observations from April 2016 to November 2018 (with some missing days) from a single camera trap (2 successive models) placed in different locations in Talissieu (Ain, France).</p> <p>See <a href="https://doi.org/10.5281/zenodo.2533381">doi:10.5281/zenodo.2533381</a> for more information.</p> <p>location : 5.7211 45.8625 (WGS84)</p> <p>fields :</p> <pre>id_obs <int> identifier date_heure <dttm> UTC date time of observation (%Y-%m-%dT%H:%M:%SZ) cd_nom_taxref <int> TAXREF taxon identifier see https://inpn.mnhn.fr/programme/referentiel-taxonomique-taxref taxref_cd_ref <int> TAXREF valid taxon identifier taxon <chr> taxon name (binomial) espece <chr> taxon name (vernacular, french) nom_complet <chr> taxon name (binomial with author) nom_complet_html <chr> taxon name (binomial italicized, with author) classe <chr> class ordre <chr> order famille <chr> family rang <chr> rank (ES : species, GN : genus, FM : family, OR : order, CL : class) effectif <int> number of individuals sexe <chr> sex type <chr> V: visual direction <chr> direction of travel (mostly blank) temperature <int> ambiant temperature (°C) duree_estimee_min <int> duration in frame (min) rem_obs <chr> observation note id_session <int> session identifier debut_session <dttm> session date time start UTC (%Y-%m-%dT%H:%M:%SZ) fin_session <dttm> session date time end UTC (%Y-%m-%dT%H:%M:%SZ) duree_session_h <int> session duration (h) rem_session <chr> session note id_localisation <int> camera trap location identifier lieu <chr> camera trap location name x_wgs84 <dbl> camera trap longitude (decimal degrees WGS84) y_wgs84 <dbl> camera trap latitude (decimal degrees WGS84) alti <int> camera trap altitude (m, NGF) azimuth <int> camera trap azimuth (°) habitat <chr> habitat type environnement <chr> local environment type cible <chr> target camera <chr> camera model id_etude <int> study identifier nom_etude <chr> study name date <date> observation date (%Y-%m-%d) heure <dbl> observation hour (%I, UTC)</pre> <p> </p>
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.
Figs 2-5 in Accessing camera trap survey feasibility for estimating Blastocerus dichotomus (Cetartiodactyla, Cervidae) demographic parameters
Figs 2-5. Individual discrimination of marsh deer males (Blastocerus dichotomus Illiger, 1815) using antler morphology: a spike-antlered male (2) and two different branched antlers (3 and 4). The arrows indicate the different horn tips. Marsh deer female accompanied by a fawn (5).
Fig. 1 in Accessing camera trap survey feasibility for estimating Blastocerus dichotomus (Cetartiodactyla, Cervidae) demographic parameters
Fig. 1. Map outlining the aerial and camera trap surveys conducted at the Jataí Ecological Station, state of SÃo Paulo, Brazil in order to obtain marsh deer demographic parameters.
Combining camera trap surveys and IUCN range maps to improve knowledge of species distributions
Open the record for dataset details and reuse information.
Behavioral “bycatch” from camera trap surveys yields insights on prey responses to human-mediated predation risk
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.
SNAPSHOT USA 2019-2023: The first five years of data from a coordinated camera trap survey of the United States
Open the record for dataset details and reuse information.
Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps
<p><a name="_Hlk58254629"></a></p> <p><a name="_Hlk58254629">Surveying cryptic, nocturnal animals is logistically challenging. Consequently, density estimates may be imprecise and uncertain. Survey innovations mitigate ecological and observational difficulties contributing to estimation variance. Thus, comparisons of survey techniques are critical to evaluate estimates of abundance. We simultaneously compared three methods for observing mountain hare (<i>Lepus timidus</i>) using Distance sampling to estimate abundance. Daylight visual surveys achieved 41 detections, estimating density at 14.3 hares km<sup>-2</sup> (95%CI 6.3–32.5) resulting in the lowest estimate and widest confidence interval. Night-time thermal imaging achieved 206 detections, estimating density at 12.1 hares km<sup>-2 </sup>(95%CI 7.6–19.4). Thermal imaging captured more observations at furthest distances, and detected larger group sizes. Camera traps achieved 3,705 night-time detections, estimating density at 22.6 hares km<sup>-2 </sup>(95%CI 17.1–29.9). Between the methods, detections were spatially correlated, although the estimates of density varied. Our results suggest that daylight visual surveys tended to underestimate density, failing to reflect nocturnal activity. Thermal imaging captured nocturnal activity, providing a higher detection rate, but required fine weather. Camera traps captured nocturnal activity, and operated 24/7 throughout harsh weather, but needed careful consideration of empirical assumptions. </a>We discuss the merits and limitations of each method with respect to the estimation of population density in the field.</p>
Figure 8 in Insights into surveying pangolins using ground and arboreal camera traps
Figure 8: Estimated detection probability with 95 % confidence interval for models run using each combination of cameras for black-bellied pangolins. For example, with 1 camera there are 6 combinations, either only camera 1, 2, 3, 4, 5, or 6. Red X's at 0 represent models that did not have enough detections for model convergence.
Figure 6 in Insights into surveying pangolins using ground and arboreal camera traps
Figure 6: Map showing the location of where black-bellied pangolin were detected during the study. Sites with a black dot were sites where cameras were placed but no black-bellied pangolin were detected, aqua are sites where 1 of the 6 cameras at the site detected the pangolin, and yellow are sites where 3 of the 6 cameras at the site detected pangolin.
Figure 7 in Insights into surveying pangolins using ground and arboreal camera traps
Figure 7: Relationship between the linear (height) effect of camera height (A), the quadratic (height + height2) effect of camera height (B), the linear (zone) effect of camera zone (C), and the quadratic (zone + zone2) effect of camera zone (D) on detection probability for black-bellied pangolin.
Figure 4 in Insights into surveying pangolins using ground and arboreal camera traps
Figure 4: Relationship between camera zone and detection probability for white-bellied pangolin using the model zone + zone2 on detection probability.
Figure 5 in Insights into surveying pangolins using ground and arboreal camera traps
Figure 5: Estimated detection probability with 95 % confidence interval for models run using each combination of cameras for white-bellied pangolins. For example, with 1 camera there are 6 combinations, either only camera 1, 2, 3, 4, 5, or 6. Red X's at 0 represent models that did not have enough detections for model convergence.
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.