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585 results for “Camera trap”
Data from: using camera traps and N-mixture models to estimate population abundance: model selection really matters
<p>Estimating the abundance or density of wildlife populations is a critical part of species conservation and management, but estimates can vary greatly in precision and accuracy according to the data collection and statistical methods, sampling and ecological variation, and sample size. N-mixture models are a common method which has been applied to a wide range of taxa for estimating population abundance from non-invasive data representing the distribution of the species. We used population estimates from an aerial survey of moose and videos from camera traps to assess the sensitivity of N-mixture models to ecological conditions, the spatial scale at which they were measured, the criteria used to define independent detections, and model choice based on the common statistical criterion of parsimony. The most parsimonious N-mixture models were considerably biased, producing implausibly large and considerably imprecise estimates of the abundance of moose. Most of the other models produced estimates of abundance that were ecologically realistic and relatively accurate. The accuracy of population estimates produced by N-mixture models were not overly sensitive to the formulation of models, the scale at which ecological conditions were measured, or the criteria used to define independent detection and by extension sample size. Our results suggest that parsimony was a poor measure of the predictive accuracy of the population estimates produced with the N-mixture model. Collecting and processing data from the aerial survey was less expensive and took less time, but data from camera traps can provide valuable information on behavior of the target species as well as insights into multiple species in the community.</p>
Data from: Shooting area of infrared camera traps affects recorded taxonomic richness and abundance of ground-dwelling invertebrates
<p>Ground-dwelling invertebrates are vital for soil biodiversity and function maintenance. Contemporary biodiversity assessment necessitates novel and automatic monitoring methods because of the threat of sharp reductions in soil biodiversity in farmlands worldwide. Using infrared camera traps (ICTs) is an effective method for assessing richness and abundance of ground-dwelling invertebrates. However, the influence that the shooting area of ICTs has on the diversity of ground-dwelling invertebrates has not been strongly considered during survey design. In this study, data from 6 ICTs with two shooting areas (A1, 38.48 cm<sup>2</sup>; A2, 400 cm<sup>2</sup>) were used to investigate ground-dwelling invertebrates in a farm in a city on the Eastern Coast of China from 20:00 on July 31 to 00:00 on September 29, 2022. Over the course of 59 days and 1,420 h, invertebrates within 9 taxa, 2,447 individuals, and 112,909 ind./m<sup>2</sup> were observed from 222,912 images. Our results show that ICTs with relatively large shooting areas recorded relatively high taxonomic richness and abundance of total ground-dwelling invertebrates, relatively high abundance of the dominant taxon, and relatively high daily and hourly abundance of most taxa. The shooting areas of ICTs significantly affected the recorded taxonomic richness and abundance of ground-dwelling invertebrates throughout the experimental period and at fine temporal resolutions. Overall, these results suggest that the shooting areas of ICTs should be considered when designing experiments, and ICTs with relatively large shooting areas are more favorable for monitoring the diversity of ground-dwelling invertebrates. This study further provides an automatic tool and high-quality data for biodiversity monitoring and protection in farmlands.</p>
Fig. 2 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania
Fig. 2. General diel activity of otters in the study area.
Fig. 1 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania
Fig. 1. The study area.
Fig. 4 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania
Fig. 4. Otter recordings, correlated with local time and day-night graph.
Using by-catch camera trapping data for estimating the population size of spotted hyena (Crocuta crocuta)
<p>Spotted hyenas (<em>Crocuta crocuta</em>) are an important carnivore species whose dual role of scavenger and predator is vital to trophic energy flows of systems in which they are found. Where populations of spotted hyenas are small, the environment has few cleaners and carcasses can remain unprocessed. Despite being largely characterized as scavengers, spotted hyenas actively hunt and take down live prey and at high densities can have depressing effects on fragile or choice ungulate populations. In addition, they can alter the structure and composition dynamics of the carnivore guild through direct conflict or indirectly through competition for food and space. Despite their importance to ecosystem function and balance, reliable estimates of spotted hyena densities are rare. This is because unlike lions and leopards, spotted hyenas are generally not regarded as a charismatic species and, as such, survey resources, which are costly, are seldom solely allocated towards surveying them. Nonetheless, being able to confidently estimate spotted hyena numbers is important for the effective management of carnivore and herbivore populations whose dynamics they influence.</p>
Data availability: Random encounter model is a reliable method for estimating population density of multiple species using camera traps
<p>Data of the paper entitled "Random encounter model is a reliable method for estimating population density of multiple species using camera traps" published on Remote Sensing in Ecology and Conservation</p>
Camera trap data used for assessment of movement patterns by injured moose
<p>The data were collected from camera trap recordings of animals on a remote trail in northern British Columbia, Canada, from 2017-2020. The data were used for an assessment of the general fauna composition of the area; and also for assessing movement patterns of moose - especially those with obvious leg injuries. The data set includes 4562 observations - that is, instances of an animal recorded on a given video. For further description see the paper "<strong><span>Performance of Wild Animals with "Broken" Traits: </span></strong><strong><span>Movement Patterns in Nature of Moose That Have Leg Injuries" published in Ecology and Evolution.</span></strong></p>
Temporal data from camera trap captures of raccoons (Procyon lotor) and coyote (Canis latrans) across urban-rural gradient Michigan 2015-2020
<p>Temporal data and trap success for raccoons (<em>Procyon lotor</em>) and coyotes (<em>Canis latrans</em>) across an urban-rural gradient in Michigan, from 2015 to 2020. These data are associated with the article "Temporal refuges of a subordinate carnivore vary across rural-urban gradient" in the journal Ecology and Evolution. </p>
Ecuadorian Plant-Hummingbird interactions over an elevation gradient in the Andes, sampled with camera traps in 11 localities
<p class="MsoNormal"><span>Community ecologists have made great advances in understanding how natural communities can be both diverse and stable by studying communities as interaction networks. However, focus has been on interaction networks aggregated over time, neglecting the consequences of the seasonal organization of interactions, henceforth seasonal structure, for community stability. Here, we extended previous theoretical findings on the topic in two ways: (i) by integrating empirical seasonal structure of 11 plant-hummingbird communities into dynamic models, and (ii) by tackling multiple facets of network stability together. We show that, in a competition context, seasonal structure enhances community stability by allowing diverse and resilient communities while preserving their robustness to species extinctions. The positive effects of empirical seasonal structure on network stability vanished when using randomized seasonal structures, suggesting that eco-evolutionary dynamics produce stabilizing seasonal structures. We also show that the effects of seasonal structure on community stability are mainly mediated by changes in network structure and productivity, suggesting that the seasonal structure of a community is an important and yet neglected aspect in the diversity-stability and diversity-productivity debates.</span></p>
Downsized camera trap images for automated classification
<b>Description: </b><p>Downsized (256x256) camera trap images used for the analyses in "Can CNN-based species classification generalise across variation in habitat within a camera trap survey?", and the dataset composition for each analysis. Note that images tagged as 'human' have been removed from this dataset. Full-size images for the BorneoCam dataset will be made available at LILA.science. The full SAFE camera trap dataset metadata is available at DOI: 10.5281/zenodo.6627707.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://safeproject.net/projects/project_view/203"><b>Machine learning and image recognition to monitor spatio-temporal changes in the behaviour and dynamics of species interactions</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (NERC QMEE CDT Studentship, NE/P012345/1, <a href="http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A">http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A</a>)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://safeproject.net/datasets/xml_metadata?id=6627707">here</a></p><p><b>Files: </b>This dataset consists of 3 files: CT_image_data_info2.xlsx, DN_256x256_image_files.zip, DN_generalisability_code.zip</p><p><b>CT_image_data_info2.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Dataset Images</b> (described in worksheet Dataset_images)</p><p>Description: This worksheet details the composition of each dataset used in the analyses</p><p>Number of fields: 69</p><p>Number of data rows: 270287</p><p>Fields: </p><ul><li><b>filename</b>: Root ID (Field type: id)</li><li><b>camera_trap_site</b>: Site ID for the camera trap location (Field type: location)</li><li><b>taxon</b>: Taxon recorded by camera trap (Field type: taxa)</li><li><b>dist_level</b>: Level of disturbance at site (Field type: ordered categorical)</li><li><b>baseline</b>: Label as to whether image is included in the baseline training, validation (val) or test set, or not included (NA) (Field type: categorical)</li><li><b>increased_cap</b>: Label as to whether image is included in the 'increased cap' training, validation (val) or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_individ_event_level</b>: Label as to whether image is included in the 'individual disturbance level datasets split at event level' training, validation (val) or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_1</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 1' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_2</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 2' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 3' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 4' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 5' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_1_2</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 2 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_1_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 3 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_1_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_1_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_2_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2 and 3 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_2_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_2_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 3 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 3 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 4 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_2_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2 and 3 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_2_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_2_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 3 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 3 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_2_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 3 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_2_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 3 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_2_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 3, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_quad_1_2_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 3 and 4 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_quad_1_2_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 3 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_quad_1_2_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_quad_1_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 3, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_quad_2_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 3, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_all_1_2_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 3, 4 and 5 (all)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_individ_1</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance level 1' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_individ_2</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance level 2' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_individ_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance level 3' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_individ_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance level 4' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_individ_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance level 5' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_1_2</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1 and 2 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_1_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1 and 3 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_1_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_1_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_2_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2 and 3 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_2_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_2_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 3 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 3 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 4 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_2_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2 and 3 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_2_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_2_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 3 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 3 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_2_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2, 3 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_2_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2, 3 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_2_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 3, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_quad_1_2_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2, 3 and 4 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_quad_1_2_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2, 3 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_quad_1_2_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_quad_1_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 3, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_quad_2_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2, 3, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_all_1_2_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2, 3, 4 and 5 (all)' training set, or not included (NA) (Field type: categorical)</li></ul></li></ol><p><b>DN_256x256_image_files.zip</b></p><p>Description: Zip file containing all images used in the analyses</p><p><b>DN_generalisability_code.zip</b></p><p>Description: Zip file containing code for the analyses</p><p><b>Date range: </b>2011-03-19 to 2018-06-12</p><p><b>Latitudinal extent: </b>4.6350 to 4.7538</p><p><b>Longitudinal extent: </b>116.9472 to 117.6253</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Aves <br> -  -  -  -  Galliformes <br> -  -  -  -  Passeriformes <br> -  -  -  -  Cuculiformes <br> -  -  -  -  -  Cuculidae <br> -  -  -  -  Columbiformes <br> -  -  -  -  -  Columbidae <br> -  -  -  -  Gruiformes <br> -  -  -  -  -  Gruidae <br> -  -  -  Mammalia <br> -  -  -  -  Proboscidea <br> -  -  -  -  -  Elephantidae <br> -  -  -  -  -  -  <i>Elephas</i> <br> -  -  -  -  -  -  -  <i>Elephas maximus</i> <br> -  -  -  -  -  -  -  -  <i>Elephas maximus borneensis</i> <br> -  -  -  -  Erinaceomorpha <br> -  -  -  -  -  Erinaceidae <br> -  -  -  -  -  -  <i>Echinosorex</i> <br> -  -  -  -  -  -  -  <i>Echinosorex gymnura</i> <br> -  -  -  -  Pholidota <br> -  -  -  -  -  Manidae <br> -  -  -  -  -  -  <i>Manis</i> <br> -  -  -  -  -  -  -  <i>Manis javanica</i> <br> -  -  -  -  Scandentia <br> -  -  -  -  -  Tupaiidae <br> -  -  -  -  -  -  <i>Tupaia</i> <br> -  -  -  -  -  -  -  <i>Tupaia dorsalis</i> <br> -  -  -  -  -  -  -  <i>Tupaia gracilis</i> <br> -  -  -  -  -  -  -  <i>Tupaia longipes</i> <br> -  -  -  -  -  -  -  <i>Tupaia tana</i> <br> -  -  -  -  Artiodactyla <br> -  -  -  -  -  Cervidae <br> -  -  -  -  -  -  <i>Muntiacus</i> <br> -  -  -  -  -  -  -  <i>Muntiacus atherodes</i> <br> -  -  -  -  -  -  -  <i>Muntiacus muntjak</i> <br> -  -  -  -  -  -  <i>Rusa</i> <br> -  -  -  -  -  -  -  <i>Rusa unicolor</i> <br> -  -  -  -  -  Suidae <br> -  -  -  -  -  -  <i>Sus</i> <br> -  -  -  -  -  -  -  <i>Sus barbatus</i> <br> -  -  -  -  -  Bovidae <br> -  -  -  -  -  -  <i>Bos</i> <br> -  -  -  -  -  -  -  <i>Bos javanicus</i> <br> -  -  -  -  -  Tragulidae <br> -  -  -  -  -  -  <i>Tragulus</i> <br> -  -  -  -  -  -  -  <i>Tragulus kanchil</i> <br> -  -  -  -  -  -  -  <i>Tragulus napu</i> <br> -  -  -  -  Rodentia <br> -  -  -  -  -  Sciuridae <br> -  -  -  -  -  -  <i>Lariscus</i> <br> -  -  -  -  -  -  <i>Sundasciurus</i> <br> -  -  -  -  -  -  -  <i>Sundasciurus hippurus</i> <br> -  -  -  -  -  -  -  <i>Sundasciurus lowii</i> <br> -  -  -  -  -  Hystricidae <br> -  -  -  -  -  -  <i>Hystrix</i> <br> -  -  -  -  -  -  -  <i>Hystrix brachyura</i> <br> -  -  -  -  -  -  -  <i>Hystrix crassispinis</i> <br> -  -  -  -  -  -  <i>Trichys</i> <br> -  -  -  -  -  -  -  <i>Trichys fasciculata</i> <br> -  -  -  -  -  Muridae <br> -  -  -  -  -  -  <i>Sundamys</i> <br> -  -  -  -  -  -  -  <i>Sundamys muelleri</i> <br> -  -  -  -  -  -  <i>Leopoldamys</i> <br> -  -  -  -  -  -  -  <i>Leopoldamys sabanus</i> <br> -  -  -  -  Primates <br> -  -  -  -  -  Hominidae <br> -  -  -  -  -  -  <i>Homo</i> <br> -  -  -  -  -  -  -  <i>Homo sapiens</i> <br> -  -  -  -  -  -  <i>Pongo</i> <br> -  -  -  -  -  -  -  <i>Pongo pygmaeus</i> <br> -  -  -  -  -  Cercopithecidae <br> -  -  -  -  -  -  <i>Presbytis</i> <br> -  -  -  -  -  -  <i>Macaca</i> <br> -  -  -  -  -  -  -  <i>Macaca fascicularis</i> <br> -  -  -  -  -  -  -  <i>Macaca nemestrina</i> <br> -  -  -  -  -  Tarsiidae <br> -  -  -  -  -  -  <i>Cephalopachus</i> <br> -  -  -  -  -  -  -  <i>Cephalopachus bancanus</i> <br> -  -  -  -  Carnivora <br> -  -  -  -  -  Felidae <br> -  -  -  -  -  -  <i>Prionailurus</i> <br> -  -  -  -  -  -  -  <i>Prionailurus bengalensis</i> <br> -  -  -  -  -  -  <i>Pardofelis</i> <br> -  -  -  -  -  -  -  <i>Pardofelis marmorata</i> <br> -  -  -  -  -  -  <i>Neofelis</i> <br> -  -  -  -  -  -  -  <i>Neofelis nebulosa</i> <br> -  -  -  -  -  -  <i>Catopuma</i> <br> -  -  -  -  -  -  -  <i>Catopuma badia</i> <br> -  -  -  -  -  Mustelidae <br> -  -  -  -  -  -  <i>Amblonyx</i> <br> -  -  -  -  -  -  -  <i>Amblonyx cinereus</i> <br> -  -  -  -  -  -  <i>Martes</i> <br> -  -  -  -  -  -  -  <i>Martes flavigula</i> <br> -  -  -  -  -  Mephitidae <br> -  -  -  -  -  -  <i>Mydaus</i> <br> -  -  -  -  -  -  -  <i>Mydaus marchei</i> <br> -  -  -  -  -  Ursidae <br> -  -  -  -  -  -  <i>Helarctos</i> <br> -  -  -  -  -  -  -  <i>Helarctos malayanus</i> <br> -  -  -  -  -  Canidae <br> -  -  -  -  -  -  <i>Canis</i> <br> -  -  -  -  -  -  -  <i>Canis lupus</i> <br> -  -  -  -  -  -  -  -  <i>Canis lupus familiaris</i> <br> -  -  -  -  -  Viverridae <br> -  -  -  -  -  -  <i>Paguma</i> <br> -  -  -  -  -  -  -  <i>Paguma larvata</i> <br> -  -  -  -  -  -  <i>Prionodon</i> <br> -  -  -  -  -  -  -  <i>Prionodon linsang</i> <br> -  -  -  -  -  -  <i>Arctictis</i> <br> -  -  -  -  -  -  -  <i>Arctictis binturong</i> <br> -  -  -  -  -  -  <i>Paradoxurus</i> <br> -  -  -  -  -  -  -  <i>Paradoxurus hermaphroditus</i> <br> -  -  -  -  -  -  <i>Hemigalus</i> <br> -  -  -  -  -  -  -  <i>Hemigalus derbyanus</i> <br> -  -  -  -  -  -  <i>Viverra</i> <br> -  -  -  -  -  -  -  <i>Viverra tangalunga</i> <br> -  -  -  -  -  Herpestidae <br> -  -  -  -  -  -  <i>Herpestes</i> <br> -  -  -  -  -  -  -  <i>Herpestes brachyurus</i> <br> -  -  -  -  -  -  -  <i>Herpestes semitorquatus</i> <br></div><p></p>
Data belonging to "Successful invasion: camera trap distance sampling reveals higher density for invasive raccoon dog compared to native mesopredators"
<p>Data files (comma separated text files) containing the camera data (CameraData) containing the information on camera trap placements in the various sites and their operation time in days and aperture, the distance sampling data (DistanceData) containing the information on the species and distance detected for each 1s time interval in front of each camera, and the trigger data (TriggerData) containing the time stamps for the pictures taken of each species with each camera, collected in the years 2020 and 2021 in southern Finland. The repository further contains an R script "distanceSamplingScript" which uses the reposited above-described files for analysis reported in the publication "Successful invasion: camera trap distance sampling reveals higher density for invasive raccoon dog compared to native mesopredators" https://doi.org/10.1007/s10530-024-03323-4. The R script has been confirmed to run in R version 4.3.3 using packages "activity" vs 1.3.4 and "Distance" vs 1.0.9</p>
Repository of camera trap data recorded during three pilot studies of the Amsterdamse Waterleidingduinen
<p>Three camera trap data packages (https://camtrap-dp.tdwg.org/) of data collected as part of pilot studies carried out in the Amsterdamse Waterleidingduinen. The pilots were aimed at determining how different types of camera deployment (e.g. regular vs. wide lens, various heights, inside/outside exclosures) might influence species detections, and how to deploy autonomous wildlife monitoring networks. Two pilots were conducted in herbivore exclosures and mainly detected European rabbits (Oryctolagus cuniculus) and red fox (Vulpes vulpes). The third pilot was conducted outside exclosures, with the European fallow deer (Dama dama) being most prevalent. Across all three pilots, a total of 47,597 images were annotated using the Agouti platform. All annotations were verified and quality-checked by a human expert. A total of 2,779 observations of 20 different species (including humans) were observed using 11 wildlife cameras during 2021–2023. The raw image files (excluding humans), image metadata, deployment metadata and observations from each pilot are shared using the Camtrap DP open standard and the extended data publishing capabilities of GBIF to increase the findability, accessibility, interoperability, and reusability of this data. The data are freely available and can be used for developing artificial intelligence (AI) algorithms that automatically detect and identify species from wildlife camera images.</p> <p><a name="_Hlk161305518"></a>The repository contains a data package in <a href="https://camtrap-dp.tdwg.org/">Camtrap DP format </a> for each of the three pilots. Camtrap DP is an open standard for the exchange and archiving of camera trap data using a standardized data structure. Each data package consists of the following resources:</p> <p>· <strong>datapackage.json:</strong> Contains metadata about the data package and camera trap project from which the data originates. Describes taxonomic, temporal, and spatial extent.</p> <p>· <strong>deployments.csv:</strong> Table of individual camera trap deployments, detailing exact location and times active of each camera deployment.</p> <p>· <strong>media.csv: </strong>Table detailing every image in the data package. Lists the filenames and paths of images within the data package.</p> <p>· <strong>observations.csv:</strong> Table of observations of species (or lack thereof) derived from the images.</p> <p>· <strong>e</strong><strong>vents.csv: </strong>Table linking observation events to media.</p> <p>· <strong>media folder:</strong> Folder containing a subfolder for each deployment, which contains the raw images from that deployment.</p> <p>Some additional notes, specific to these datasets:</p> <p>· The deployment table contains “deployment tags”, which specify extra information about the deployment, formatted as key:value pairs, separated by pipes (‘|’). Of particular interest for these datasets are the tags that state lens angle, specify habitat type and identify paired cameras (e.g. to assess differences in species detections between cameras with regular and wide lens, respectively).</p> <p>· In all three pilots, most observations are linked to sequences of images recorded within 120 seconds of each other. Hence, observations in these datasets are generally linked to an “event” (i.e. a sequence of images) rather than to an individual media file. We have added an events table to more easily link observation events and the media items that make up that event. This is an extension of the camera trap DP standard.</p> <p>· All annotations were verified and checked by a human expert, even in cases where an observation is listed as being made by an AI algorithm.</p> <p>· Whether or not an image is included in the data package is indicated by the ‘filePublic’ column in the media table. All raw images are included except for those where humans were detected. Images in which humans were detected have a ‘filePublic’ value of FALSE. Although the current location of these files within the Agouti platform (<a href="https://www.agouti.eu/">https://www.agouti.eu/</a>) is recorded in the ‘filePath’ column, these files cannot be accessed. The ‘fileName’ of these filles is the original filename they possessed when uploaded to Agouti.</p> <p>· Where ‘filePublic’ is TRUE, the `filePath` given is relative to the root of the data package (e.g. ‘media/<deployment>’) and the `fileName` of the file is the current name of the file within the data package (‘<mediaID>.JPG’). </p> <p>More details about individual metadata fields in the Camtrap DP format can be found on <a href="https://camtrap-dp.tdwg.org/">https://camtrap-dp.tdwg.org/</a>.</p> <p> </p> <p> </p>
Assessing the potential of camera traps for estimating activity pattern compared to collar-mounted activity sensors: A case study on Eurasian lynx (Lynx lynx) in South-Eastern Norway
<div> <div> <div> <div> <p>The diel activity patterns of animals convey information about physiology, ecological niches and animal behaviour relevant for both applied conservation and more theoretical research. However, these patterns are challenging to study in the field. The current gold-standard approach to quantify the movements and activity patterns of medium to large wildlife species is to use Global Positioning Systems (GPS) collars equipped with activity sensors (e.g., accelerometers). A more recent approach consists of inferring activity patterns from the time-stamped pictures of wildlife obtained from the camera traps now routinely used in wildlife monitoring projects. However, few studies have attempted to validate estimates of activity patterns obtained from camera traps against those obtained from activity sensors. In this study, we compared the diel activity pattern of the Eurasian lynx Lynx lynx inferred from detections by a network of over 300 camera traps active between 2010 and 2020, to activity patterns obtained from 18 GPS-collared lynx (8 females, 10 males) equipped with 2-axis accelerometer sensors, in the same area of southern Norway. Our results suggest that camera traps can be used to estimate diel activity curves that are comparable to those obtained from accelerometers. In our study 75 detections were sufficient to approximate the diel activity pattern obtained from accelerometer. Subsampling indicated that a low number of detections results in a coarser approximation of the diel activity pattern.</p> </div> </div> </div> </div>
Remote camera monitoring and arboreal trapping data for a reintroduced population of red-tailed phascogales (Phascogale calura)
<p>Effective monitoring methods are required to evaluate the success of wildlife reintroduction programs. To improve the threat status of the Vulnerable red-tailed phascogale (<em>Phascogale calura</em>), the Australian Wildlife Conservancy reintroduced the species to a fenced reserve at Mt. Gibson Wildlife Sanctuary. After trialing a variety of post-release monitoring methods, remote camera monitoring and arboreal trapping with an extensive period of pre-luring provided the most information with which to evaluate the success of the reintroduction. To date, reintroduced red-tailed phascogales have increased in both occupancy and population size following releases which began at Mt. Gibson in 2017. Other managers of red-tailed phascogale populations may find the described methods useful, particularly in the context of multi-species reintroductions where trap saturation can reduce capture rates of smaller species, such as phascogales.</p>
Combining local ecological knowledge with camera traps to assess the link between African mammal life history traits and their occurrence in anthropogenic landscapes
<p>Understanding what influences species and trait composition is critical for predicting changes in communities driven by landscape transformation. </p> <p>We explored how life history traits are associated with the persistence of mammal species in human-dominated habitats within the Garden Route Biosphere Reserve, South Africa. We combined data from a camera trap and a local ecological knowledge-based survey in an integrated occupancy model to analyze species occurrence along a gradient of anthropogenic landscape transformation. </p> <p>Results confirmed that mammal occurrence in human-modified habitats was related to specific life history traits. Species with more specialist diets, as well as larger body mass species were more likely to stay in protected areas. Species with slow reproductive strategies occupied more natural areas. </p> <p>Our study also showed that combining different monitoring methods enabled us to increase spatial coverage and mammal sighting numbers. This approach fostered research participation by various stakeholders, an important step for co-designing wildlife-friendly anthropogenic spaces. </p> <p><strong>Synthesis and applications: </strong>Integrating data from a standard ecological protocol and structured participatory citizen knowledge allowed us to identify the species functional traits associated with mammal species occurrence in anthropogenic landscapes at a local scale. These results advocate for wisely combining methods, and will guide conservation orientated land-use planning towards the protection of natural habitats in the Garden Route Biosphere Reserve. This methodological approach will enable managers and conservationists to use data obtain from diverse protocols. This should catalyze the involvement of citizens in biodiversity monitoring and conservation.</p>
Long term bearded pig camera trap data across the SAFE landscape.
<p><strong>Description: </strong></p> <p>Data on camera trap surveys and capture events for bearded pigs across the SAFE landscape from 2011-2017.<br> Data was collected by Dr Oliver Wearn from 2011 to 2014, by Phil Chapman from 2015 to 2016 and by Charlie Davison in 2017.<br> Used to assess how bearded pigs are responding to land-use change in Sabah. NB: These data are a subset of the full SAFE Project core mammal trapping data, but include additional details about bearded pig social structure and abundances.</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/173"><strong>Group Dynamics of Bornean Bearded Pigs: the advantages of behavioural plasticity in changeable landscapes.</strong></a></p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=312">here</a></p> <p><strong>Files: </strong>This consists of 1 file: DavisonBeardedPigs.xlsx</p> <p><strong>DavisonBeardedPigs.xlsx</strong></p> <p>This file contains dataset metadata and 2 data tables:</p> <ol> <li> <p><strong>Deployments</strong> (described in worksheet Deployments)</p> <p>Description: Data relating to all random camera trap deployments</p> <p>Number of fields: 5</p> <p>Number of data rows: 833</p> <p>Fields:</p> <ul> <li><strong>TrapID</strong>: Camera placement point on SAFE project core grids (Field type: Location)</li> <li><strong>Date.On</strong>: Date survey started (Field type: Date)</li> <li><strong>Date.Off</strong>: Date survey ended (Field type: Date)</li> <li><strong>CTNs</strong>: Length of survey (camera trap nights). Zero if camera was faulty. (Field type: Numeric)</li> <li><strong>Landuse</strong>: Land-use type (Field type: Categorical)</li> </ul> </li> <li> <p><strong>Records</strong> (described in worksheet Records)</p> <p>Description: Data relating to all camera trap records of bearded pigs across all land-uses, and humans and domestic dogs in Oil palm; data generated from individual camera trap images</p> <p>Number of fields: 13</p> <p>Number of data rows: 4236</p> <p>Fields:</p> <ul> <li><strong>TrapID</strong>: Camera placement location on SAFE project core grids (Field type: Location)</li> <li><strong>Date</strong>: Date of photo capture (Field type: Date)</li> <li><strong>Time</strong>: Time of photo capture (Field type: Time)</li> <li><strong>Ambient.Temp</strong>: Temperature at the time of photo capture (Field type: Numeric)</li> <li><strong>Moon.Phase</strong>: Moonphase at time of photo capture (Field type: Categorical)</li> <li><strong>Species</strong>: Identity of the individual(s) (Field type: Taxa)</li> <li><strong>Soc.Str</strong>: Social structure (Field type: Categorical)</li> <li><strong>Sex</strong>: Sex of the individual (Field type: Categorical)</li> <li><strong>No.Individuals</strong>: Number of individuals in survey (Field type: Abundance)</li> <li><strong>No.Juveniles</strong>: Number of adults in survey (Field type: Abundance)</li> <li><strong>No.Subadults</strong>: Number of subadults in survey (Field type: Abundance)</li> <li><strong>No.Adults</strong>: Number of juveniles in survey (Field type: Abundance)</li> <li><strong>Land-use</strong>: Land use type (Field type: Categorical)</li> </ul> </li> </ol> <p><strong>Date range: </strong>2011-04-30 to 2018-04-01</p> <p><strong>Latitudinal extent: </strong>4.6350 to 4.7538</p> <p><strong>Longitudinal extent: </strong>116.9472 to 117.6253</p> <p><strong>Taxonomic coverage: </strong><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p> <p>Animalia<br>  - Chordata<br>  -  - Mammalia<br>  -  -  - Artiodactyla<br>  -  -  -  - Suidae<br>  -  -  -  -  - <em>Sus</em><br>  -  -  -  -  -  - <em>Sus barbatus</em><br>  -  -  - Carnivora<br>  -  -  -  - Canidae<br>  -  -  -  -  - <em>Canis</em><br>  -  -  -  -  -  - <em>Canis lupus</em><br>  -  -  -  -  -  -  - <em>Canis lupus familiaris</em><br>  -  -  - Primates<br>  -  -  -  - Hominidae<br>  -  -  -  -  - <em>Homo</em><br>  -  -  -  -  -  - <em>Homo sapiens</em></p> <p> </p>
Camera trap image of Phylloscopus collybita (2016-01-18T19:05:44Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Lepus europaeus (2019-02-01T14:20:22Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Felis silvestris catus (2017-09-11T12:16:10Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
ScienceDex guides
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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.