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A motion‐detection based camera trap for small nocturnal mammals with low latency and high signal‐to‐noise ratio
Author(s) -
Klemens Jeffrey A.,
Tripepi Manuela,
McFoy Shane A.
Publication year - 2021
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.13607
Subject(s) - camera trap , noise (video) , latency (audio) , signal (programming language) , environmental science , computer science , wildlife , remote sensing , real time computing , computer vision , ecology , geography , biology , telecommunications , image (mathematics) , programming language
Camera traps are useful for monitoring wildlife populations, but traps may not always trigger when targeting small, nocturnal species. Motion‐detection techniques have advantages over time‐lapse and heat‐triggered traps, but need to be deployed to maximize signal‐to‐noise ratio. As part of a study of flying squirrels ( Glaucomys ) in urban environments we developed motion‐detecting camera traps using a raspberry pi microcomputer and camera and a 940 nm IR illuminator on a tree‐mounted wooden platform. The system was built from commercially available parts and was comparable in cost to a consumer camera trap, although this cost did not include a waterproof housing. We compared the performance of our system to commercial trailcams. Four pi cameras successfully documented visits by Glaucomys and other animals to bait placed on the platform over three nights at four wooded sites: suburban and rural backyards, a private outdoors club and a small urban nature reserve (16 camera X site combinations, 48 trap nights). The traps showed low latency, with an average of <1 night until detection of Glaucomys at each site. Data collected had a high signal‐to‐noise ratio; of 2,182 capture events 55% documented Glaucomys , 40% documented non‐target mammals, 1% were caused by large insects and the remaining 4% were unknown. Commercial camera traps placed at the sites failed to capture many of these events. The low cost and high signal‐to‐noise ratio of this system may make it easy to adapt for other small animal applications. The main modification required to deploy this system in new situations will be in locating or providing a fixed or static background against which animals can be observed and in using masking techniques within motion‐detection software.