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Abstract 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.
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Michigan ZoomIN: Validating Crowd‐Sourcing to Identify Mammals from Camera Surveys
ABSTRACT Camera trap studies have become a popular medium to assess many ecological phenomena including population dynamics, patterns of biodiversity, and monitoring of endangered species. In conjunction with the benefit to scientists, camera traps present an unprecedented opportunity to involve the public in scientific research via image classifications. However, this engagement strategy comes with a myriad of complications. Volunteers vary in their familiarity with wildlife, thus, the accuracy of user‐derived classifications may be biased by the commonness or popularity of species and user‐experience. From an extensive multi‐site camera trap study across Michigan, U.S.A, we compiled and classified images through a public science platform called Michigan ZoomIN. We aggregated responses from 15 independent users per image using multiple consensus methods to assess accuracy by comparing to species identification completed by wildlife experts. We also evaluated how different factors including consensus algorithms, study area, wildlife species, user support, and camera type influenced the accuracy of user‐derived classifications. Overall accuracy of user‐derived classification was 97%; although, several canid (e.g.,Canis lupus, Vulpes vulpes) and mustelid (e.g.,Neovison vison) species were repeatedly difficult to identify by users and had lower accuracy. When validating user‐derived classification, we found that study area, consensus method, and user support best explained accuracy. To overcome hesitancy associated with data collected by untrained participants, we demonstrated their value by showing that the accuracy from volunteers was comparable to experts when classifying North American mammals. Our hierarchical workflow that integrated multiple consensus methods led to more image classifications without extensive training and even when the expertise of the volunteer was unknown. Ultimately, adopting such an approach can harness broader participation, expedite future camera trap data synthesis, and improve allocation of resources by scholars to enhance performance of public participants and increase accuracy of user‐derived data. © 2021 The Wildlife Society.
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- Award ID(s):
- 2005812
- PAR ID:
- 10240927
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Wildlife Society Bulletin
- Volume:
- 45
- Issue:
- 2
- ISSN:
- 2328-5540
- Format(s):
- Medium: X Size: p. 221-229
- Size(s):
- p. 221-229
- Sponsoring Org:
- National Science Foundation
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