DOI: 10.5061/dryad.k0p2ngfhn
title: SNAPSHOT USA 2019-2023: The first five years of data from a coordinated camera trap survey of the United States

description: SNAPSHOT USA is an annual, multi-contributor camera trap survey of mammals
 across the United States. The growing SNAPSHOT USA dataset is intended for
 tracking the spatial and temporal responses of mammal populations to
 changes in land use, land cover, and climate. These data will be useful
 for exploring the drivers of spatial and temporal changes in relative
 abundance and distribution, as well as the impacts of species interactions
 on daily activity patterns. SNAPSHOT USA 2019–2023 contains
 987,979 records of camera trap image sequence data and 9,694 records of
 camera trap deployment metadata. Data were collected across the
 United States of America in all 50 states, 12 ecoregions, and many
 ecosystems. Data were collected between August 1st and December
 29th each year from 2019 to 2023. The dataset includes a wide
 range of taxa but is primarily focused on medium to large
 mammals. SNAPSHOT USA 2019–2023 comprises two .csv files. The
 original data can be found within the SNAPSHOT USA Initiative in the
 Wildlife Insights platform.
description: The first three annual SNAPSHOT USA surveys were coordinated by
 Roland Kays, Michael Cove, and William McShea. The 2019, 2020, and 2021
 datasets are accessible for public use through the Supporting Information
 of their respective publications. Although the 2019 and 2020 surveys were
 originally processed and stored in eMammal (https://www.emammal.si.edu),
 all data are now housed in Wildlife Insights (WI) within the SNAPSHOT USA
 Initiative. The two most recent surveys, 2022 and 2023, were coordinated
 by the SNAPSHOT USA Survey Coordinator Brigit Rooney. This dataset
 represents the first publication of 2022 and 2023 SNAPSHOT USA
 data. The SNAPSHOT USA project developed a standard
 protocol in 2019 to survey mammals &gt;100 g and large identifiable
 birds. Cameras are unbaited and set at approximately 50 cm height across
 an array of at least 7 cameras with a minimum distance of 200 m and a
 maximum of 5 km between them. The collection period for SNAPSHOT USA data
 is between September and October and the target minimum of camera
 trap-nights per array is 400. Some contributors to SNAPSHOT USA 2019–2023
 started collecting data earlier or deployed cameras later based on
 locations or logistics, and we chose to include data from August
 1<sup>st</sup> through December 29<sup>th</sup>
 each year in this dataset. The first two years of
 SNAPSHOT USA data incorporated an Expert Review Tool to verify the
 accuracy of every identification, as that was built in to the eMammal
 repository. This tool required SNAPSHOT USA project managers (Cove and
 Kays in 2019, with more taxon-specific reviewers in 2020) to review and
 confirm all species identifications, in an effort to minimize
 identification errors. As eMammal automatically grouped all uploaded
 images into “sequences” of images taken within 60 seconds of each other,
 by using the image timestamps, species identifications were made for
 individual sequences rather than images. These data have since been
 transferred to WI, where they underwent opportunistic review and
 correction by the SNAPSHOT USA Survey Coordinator. In
 contrast, SNAPSHOT USA 2021, 2022, and 2023 were managed and identified
 entirely in WI. All SNAPSHOT USA projects in this repository were created
 as “Sequence” projects, to enable the identification of sequences in the
 same manner as eMammal. Each 60-second sequence of images was classified
 to the narrowest taxonomic level possible by three iterations of
 validation. First, WI’s Artificial Intelligence algorithm suggested a
 taxonomic identification. This algorithm consists of a multiclass
 classification deep convolutional neural network model that uses
 pre-trained image embedding from Inception, a model used to identify
 objects. Second, each array’s Principal Investigator was responsible for
 validating the data, fixing Artificial Intelligence identification
 mistakes, and approving the data they contributed to the survey. Lastly,
 the SNAPSHOT USA Survey Coordinator quality-checked the deployment data
 and as many identified sequences as possible. This was a multistep process
 that began with checking the sequence metadata for obvious timestamp
 errors by organizing them chronologically in Microsoft Excel, and the
 deployment metadata for location errors by mapping their coordinates and
 looking for outliers. Next, the coordinator checked the sequence metadata
 for unlikely identifications, including species detections in places
 outside their known range, and verified their accuracy by viewing the
 images in WI. Finally, identifications for the most common species were
 verified by using the “Species” filter on WI to look for mistakes, one
 species at a time.  When combining the five years of
 SNAPSHOT USA data to create SNAPSHOT USA 2019–2023, several aspects of the
 data were standardized to ensure consistency across all years. These were
 camera array names, camera location names, and taxonomy classifications.
 To match protocol requirements, all camera locations less than 5 km apart
 were classified as one array. This resulted in combining several arrays
 that were originally recorded under different names and ensuring that
 arrays in the same place maintained the same name each year. The camera
 location names were standardized by ensuring that all locations with
 geographic coordinates that were the same to four decimal places, in
 Decimal Degrees notation, had the same name. However, the original
 coordinates were retained in the dataset. Finally, all species taxonomy
 classifications for the 2019 and 2020 datasets (identified in eMammal)
 were standardized to match those used by WI. As part of this process, all
 subspecies of mammals in the dataset were changed to species level (e.g.,
 Florida black bear (<em>Ursus americanus floridanus</em>)
 became American black bear (<em>Ursus
 americanus</em>)). For mammal taxonomy
 classifications, WI uses a combination of the International Union for
 Conservation of Nature (IUCN) Red List of Threatened Species (2023;
 https://iucnredlist.org) and the American Society of Mammalogists Mammal
 Diversity Database (2024; https://www.mammaldiversity.org). For bird
 species, WI uses Birdlife International’s taxonomy classifications (2024;
 https://datazone.birdlife.org/species/search). The WI taxonomy is
 continually updated in response to public user suggestions and the
 taxonomy used in the SNAPSHOT USA 2019–2023 dataset reflects the WI
 taxonomy used in June 2024.
description: # SNAPSHOT USA 2019-2023: The first five years of data from a coordinated
 camera trap survey of the United States
 [https://doi.org/10.5061/dryad.k0p2ngfhn](https://doi.org/10.5061/dryad.k0p2ngfhn) ## Description of the data and file structure ### Files and variables #### File: ssusa\_finalsequences.csv **Description:** This file contains all the image sequence data from Snapshot USA 2019-2023, including species taxonomy information. The data can be connected to the deployments file by the Deployment ID column. **NA values:** In this file, "NA" means "Not Applicable." All taxonomic variables except for "Common_Name" contain some NA cells because the species identifications contain varying levels of taxonomic information. An identification may be "NA" for taxonomic Class if it is a non-animal identification, such as Vehicle. The variables "Age" and "Sex" also contain NA cells, and these refer to instances where it was not possible for the image tagger to identify the age and/or sex of the animal(s). ##### Variables * Year: Number between 2019 and 2023. Each year represents the year the associated data were collected. * Project: One of 20 potential text strings representing the different Wildlife Insights (WI) project names herein. * Camera_Trap_Array: One of 263 potential text strings representing the different camera trap arrays herein. Each camera trap array represents a subproject in WI.  * Deployment_ID: One of 9,694 potential text strings associated with specific camera trap deployments across sites. Most sites are associated with a single Deployment_ID, but camera photos could be uploaded in batches corresponding with multiple WI deployments from the same site. * Sequence_ID: Text string associated with a specific observation from a camera trap site. Observations are a sequence of all camera trap photos within one minute of a single camera trigger. Multiple species will be identified in separate rows for the same Sequence_ID, so Sequence_ID can repeat. * Start_Time: Timestamp of the first camera trap photo in each camera trap sequence. Provided in the following format: “YYYY-MM-DD HH:MM:SS”. * End_Time: Timestamp of first camera trap photo in each camera trap sequence. Provided in the following format: “YYYY-MM-DD HH:MM:SS”. * Class: Taxonomic class of the animal observed in the sequence. * Order: Taxonomic order of the animal observed in the sequence. * Family: Taxonomic family of the animal observed in the sequence. * Genus: Taxonomic genus of the animal observed in the sequence. * Species: Taxonomic specific epithet that distinguishes the species within the genus of the animal observed in the sequence. * Common_Name: Common name of the species observed in the sequence. * Age: Age category of animal observed, if distinguishable by the observer. The default is unknown since this was not consistently recorded. Options are Adult, Juvenile, and Unknown. * Sex: Sex category of animal observed, if distinguishable by the observer. The default is unknown since this was not consistently recorded. Options are Female, Male, and Unknown. * Group_Size: Integer between 1 and 50. The number of individuals observed in a single camera trap sequence observation. #### File: ssusa\_finaldeployments.csv **Description:** This file contains all deployment metadata from Snapshot USA 2019-2023. Each row represents one camera trap deployment.  ##### Variables * Year: Double precision vector between 2019 and 2023. Each year represents the year the associated data were collected. * Project: One of 20 potential text strings representing the different WI project names herein. * Camera_Trap_Array: One of 263 potential text strings representing the different camera trap arrays herein. Each camera trap array represents a subproject in WI.  * Site_Name: One of 6,712 potential text strings associated with specific camera trap locations within each subproject. * Deployment_ID: One of 9,694 potential text strings associated with specific camera trap deployments. * Start_Date: Date camera was placed, provided in the format: “YYYY-MM-DD”. * End_Date: Date camera was retrieved, provided in the format: “YYYY-MM-DD”. * Survey_Nights: Number of nights the camera was active at that site. * Latitude: Double precision vector between 21.3558 and 59.4526. All geographic coordinates are provided in decimal degrees (WGS 84). * Longitude: Double precision vector between -157.7496 and -68.6116. All geographic coordinates are provided in decimal degrees (WGS 84). * Habitat: Provided by contributing authors, this indicates if the array was classified as forest, grassland, shrubland, chaparral, desert, wetland, or beach. * Development_Level: Provided by contributing authors, this indicates if the array was classified as wild, rural, suburban, or urban. * Feature_Type: One of 18 potential text strings associated with feature types. Denotes any potential features at the camera site, including water source, road (dirt or paved), and trail (game or hiking). ## Code/software NA ## Access information Other publicly accessible locations of the data: * Most of the data are available to download from 5 separate projects within the Snapshot USA Initiative on Wildlife Insights. The rest of the data are within 15 other Wildlife Insights projects, and their names are provided in the "Project" column in the data files. The data can be downloaded by anyone with a Wildlife Insights account (free to obtain) through the Wildlife Insights Explore page: [https://app.wildlifeinsights.org/explore](https://app.wildlifeinsights.org/explore). ## Version Changes **10-April-2025:** Re-ran the data cleaning scripts in R because an error in the original scripts caused the accidental omission of 15,564 rows of data from the Sequences file. These belonged to 13 different camera arrays in 2019: USAFA, NyeHolman, Storrs, Herrera, Wilmington, Melrose, CrocLake, Carrollton, LowerAltmaha, Oahu, Rentz, Galena, and Hailey. Also re-ran the Deployments data, but the only change was to remove deployments that were surveyed for 0 days, since those were also removed from the Sequences data file. This change removed 15 deployments from the Deployments file.