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Title: SNAPSHOT USA 2021: A third coordinated national camera trap survey of the U nited S tates

SNAPSHOT USA is a multicontributor, long‐term camera trap survey designed to survey mammals across the United States. Participants are recruited through community networks and directly through a website application ( The growing Snapshot dataset is useful, for example, for tracking wildlife population responses to land use, land cover, and climate changes across spatial and temporal scales. Here we present the SNAPSHOT USA 2021 dataset, the third national camera trap survey across the US. Data were collected across 109 camera trap arrays and included 1711 camera sites. The total effort equaled 71,519 camera trap nights and resulted in 172,507 sequences of animal observations. Sampling effort varied among camera trap arrays, with a minimum of 126 camera trap nights, a maximum of 3355 nights, a median 546 nights, and a mean 656 ± 431 nights. This third dataset comprises 51 camera trap arrays that were surveyed during 2019, 2020, and 2021, along with 71 camera trap arrays that were surveyed in 2020 and 2021. All raw data and accompanying metadata are stored on Wildlife Insights (, and are publicly available upon acceptance of the data papers. SNAPSHOT USA aims to sample multiple ecoregions in the United States with adequate representation of each ecoregion according to its relative size. Currently, the relative density of camera trap arrays varies by an order of magnitude for the various ecoregions (0.22–5.9 arrays per 100,000 km2), emphasizing the need to increase sampling effort by further recruiting and retaining contributors. There are no copyright restrictions on these data. We request that authors cite this paper when using these data, or a subset of these data, for publication. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.

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Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Medium: X
Sponsoring Org:
National Science Foundation
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    Terrestrial Parasite Tracker indexed biotic interactions and review summary.

    The Terrestrial Parasite Tracker (TPT) project began in 2019 and is funded by the National Science foundation to mobilize data from vector and ectoparasite collections to data aggregators (e.g., iDigBio, GBIF) to help build a comprehensive picture of arthropod host-association evolution, distributions, and the ecological interactions of disease vectors which will assist scientists, educators, land managers, and policy makers. Arthropod parasites often are important to human and wildlife health and safety as vectors of pathogens, and it is critical to digitize these specimens so that they, and their biotic interaction data, will be available to help understand and predict the spread of human and wildlife disease.

    This data publication contains versioned TPT associated datasets and related data products that were tracked, reviewed and indexed by Global Biotic Interactions (GloBI) and associated tools. GloBI provides open access to finding species interaction data (e.g., predator-prey, pollinator-plant, pathogen-host, parasite-host) by combining existing open datasets using open source software.

    If you have questions or comments about this publication, please open an issue at or contact the authors by email.

    The creation of this archive was made possible by the National Science Foundation award "Collaborative Research: Digitization TCN: Digitizing collections to trace parasite-host associations and predict the spread of vector-borne disease," Award numbers DBI:1901932 and DBI:1901926

    Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics.

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     - Denver Museum of Nature and Science (DMNS) Parasite specimens (DMNS:Para) accessed via on 2022-06-24T14:08:00.730Z
     - Field Museum of Natural History IPT accessed via on 2022-06-24T14:18:51.995Z
     - Illinois Natural History Survey Insect Collection accessed via on 2022-06-24T14:19:37.563Z
     - UMSP / University of Minnesota / University of Minnesota Insect Collection accessed via on 2022-06-24T14:20:27.232Z
     - Milwaukee Public Museum Biological Collections Data Portal accessed via on 2022-06-24T14:20:46.185Z
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     - Scott L. Gardner and Gabor R. Racz (2021). University of Nebraska State Museum - Parasitology. Harold W. Manter Laboratory of Parasitology. University of Nebraska State Museum. accessed via on 2022-06-24T16:13:06.914Z
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    A web server to look through predictions is available through

    Dataset Organization

    The contains 11,000 shapefiles, each corresponding to a 1km^2 RGB tile from NEON (ID: DP3.30010.001). For example "2019_SOAP_4_302000_4100000_image.shp" are the predictions from "2019_SOAP_4_302000_4100000_image.tif" available from the NEON data portal: NEON's file convention refers to the year of data collection (2019), the four letter site code (SOAP), the sampling event (4), and the utm coordinate of the top left corner (302000_4100000). For NEON site abbreviations and utm zones see 

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    Weinstein, B. G., Marconi, S., Bohlman, S. A., Zare, A., & White, E. P. (2020). Cross-site learning in deep learning RGB tree crown detection. Ecological Informatics56, 101061.

    Weinstein, B., Marconi, S., Aubry-Kientz, M., Vincent, G., Senyondo, H., & White, E. (2020). DeepForest: A Python package for RGB deep learning tree crown delineation. bioRxiv.

    Weinstein, Ben G., et al. "Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks." Remote Sensing 11.11 (2019): 1309.

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    # Contact

    We welcome questions, ideas and general inquiries. The data can be used for many applications and we look forward to hearing from you. Contact 

    Gordon and Betty Moore Foundation: GBMF4563 
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