Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data

Camera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap-derived Big Data are becoming increasingly solvable with the help of scalable cyber-infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, “Camera Trap Metadata Standard” (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media-based and event-based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in-depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard. Biodiversity data, camera traps, data exchange, data sharing, information standardspublishedVersio

Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data

Camera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap-derived Big Data are becoming increasingly solvable with the help of scalable cyber-infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, “Camera Trap Metadata Standard” (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media-based and event-based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in-depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standar

SNAPSHOT USA 2019 : a coordinated national camera trap survey of the United States

This article is protected by copyright. All rights reserved.With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14-week period (17 August - 24 November of 2019). We sampled wildlife at 1509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian's eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the USA. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban-wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot-usa, as well as future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species-specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication.Publisher PDFPeer reviewe

CameraStationVegetationData

This file contains all the vegetation measurements taken at each camera station pair. Cameras were set up in the Summer and Fall seasons of 2013 and 2014 on the grounds of the Smithsonian Conservation Biology Institute in Front Royal, VA USA. Cameras were only established in forest habitat. Camera stations were made up of pairs of cameras, one at a log feature or game trail, the other at a random nearby location. Vegetation measurements were taken at the midpoint between the two cameras. In addition to the PairID, Grid location, and vegetation sampling date, 4 vegetation variables are included. "CovAll" is the overall level vegetative cover percentage recorded as the average percent of a 2-m high cover pole obscured from 10 m away in each cardinal direction. Cover pole was divided in 20 1-dm sections. Data were recorded as the percentage of 1-dm sections obscured ( >50%) by vegetative or structural cover. "CovLow" is the average percent of low portion (1.5 m in height and 7.5 cm dbh counted within 3 arm's-length transects, divided by 33 square meters. Transects used were same as for "UnderStem_D"

Data from: Camera trap placement and the potential for bias due to trails and other features

Camera trapping has become an increasingly widespread tool for wildlife ecologists with large numbers of studies relying on photo capture rates or presence/absence information. It is increasingly clear that camera placement can directly impact this kind of data, yet these biases are poorly understood. We used a paired camera design to investigate the effect of small-scale habitat features on species richness estimates, and capture rate and detection probability of mammal species in the Shenandoah Valley of Virginia, USA. Cameras were deployed at either log features or on game trails with a paired camera at a nearby random location. Overall capture rates were significantly higher at trail and log cameras compared to their paired random cameras, and some species showed capture rate increases as high as 9.7 times greater at feature-based cameras. We recorded more species at both log (17) and trail features (15) than at their paired control sites (13 and 12 species, respectively) yet richness estimates were indistinguishable after 659 and 385 camera nights of survey effort, respectively, for log and trail features. We detected significant increases (ranging from 11-33%) in detection probability for 5 species resulting from the presence of game trails. Detection probability was also influenced by the presence of a log feature for six species. Bias was most pronounced for the three rodents investigated, where in all cases detection probability was substantially higher (24.9-38.2%) at log cameras. Our results indicate that small-scale factors, including the presence of game trails as well as other features, can have significant impacts on the frequency and probability of species detection when camera traps are employed. Significant biases may result if the presence and quality of these features are not documented and either incorporated into analytical procedures, or controlled for in study design

Predator identity and forage availability affect predation risk of juvenile black-tailed deer

No description supplie

AllMammalPhotos_archive

This file contains a row for each photo image recorded on camera traps during this study. The study was conducted during Summer and Fall months of 2013 and 2014 on the grounds of the Smithsonian Conservation Biology Institute in Front Royal, Virginia USA. All records of birds and humans have been removed. Cameras were established in pairs with a treatment camera (set up with a log in view, or on a game trail) and a nearby random location. End Date refers to the date after which at last one camera in the pair stopped functioning. All photo records from BOTH cameras in the pair taken after this date were removed. The grounds of the study area were divided into grids (500m by 500m) and grids not containing forest were not used. Grid codes are included with each image as is the UTM coordinate of the camera station (UTM Zone 17). "UnderCat" is a three level descriptor for level of understory vegetation at the site. "LogD" refers to log diameter in centimeters, and "TrailQ" is a scale of trail quality, with 1 being the highest quality. "PairDist_m" is the distance between the two cameras in the pair, in meters. All other details are explained in the manuscript. NOTE: Photograph capture times for deployment SCBI2013.4T are 7 hrs ahead of the actual photo time. The Date and Time listed for photos from Deployment SCBI2013.37C are incorrect and could be rectified. Photos all occurred between the deployment and pull dates, but times and actual dates are incorrect for each photo

Model results showing the effect of setting a camera on a feature (game trail or log) based on paired comparisons with nearby random locations.

<p>Model results showing the effect of setting a camera on a feature (game trail or log) based on paired comparisons with nearby random locations.</p

Sample-based species accumulation curve from 24 paired cameras, run for an average of 25.6 camera nights, with one camera in each pair oriented toward a trail feature, and the other at a nearby (mean = 23.5 m) random location.

<p>Shaded areas represent the 95% confidence intervals, with darker shaded areas representing confidence interval overlap between the two scenarios. Black dots indicate the actual sampling effort of this study. The vertical line with label indicates the point at which the confidence intervals of the feature and control groups overlap.</p