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Treefrog lateral line as a mean of individual identification through visual and software assisted methodologies

  • Kim, Mi Yeon (Department of Life Sciences and Division of EcoScience, Ewha Womans University) ;
  • Borzee, Amael (Department of Life Sciences and Division of EcoScience, Ewha Womans University) ;
  • Kim, Jun Young (Department of Life Sciences and Division of EcoScience, Ewha Womans University) ;
  • Jang, Yikweon (Department of Life Sciences and Division of EcoScience, Ewha Womans University)
  • Received : 2017.06.27
  • Accepted : 2017.12.07
  • Published : 2017.12.31

Abstract

Background: Ecological research often requires monitoring of a specific individual over an extended period of time. To enable non-invasive re-identification, consistent external marking is required. Treefrogs possess lateral lines for crypticity. While these patterns decrease predator detection, they also are individual specific patterns. In this study, we tested the use of lateral lines in captive and wild populations of Dryophytes japonicus as natural markers for individual identification. For the purpose of the study, the results of visual and software assisted identifications were compared. Results: In normalized laboratory conditions, a visual individual identification method resulted in a 0.00 rate of false-negative identification (RFNI) and a 0.0068 rate of false-positive identification (RFPI), whereas Wild-ID resulted in RFNI = 0.25 and RFNI = 0.00. In the wild, female and male data sets were tested. For both data sets, visual identification resulted in RFNI and RFPI of 0.00, whereas the RFNI was 1.0 and RFPI was 0.00 with Wild-ID. Wild-ID did not perform as well as visual identification methods and had low scores for matching photographs. The matching scores were significantly correlated with the continuity of the type of camera used in the field. Conclusions: We provide clear methodological guidelines for photographic identification of D. japonicus using their lateral lines. We also recommend the use of Wild-ID as a supplemental tool rather the principal identification method when analyzing large datasets.

Acknowledgement

Supported by : Rural Development Administration

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