Deakin University
Browse

Investigating the viability of photo-identification as an objective tool to study endangered sea turtle populations

Version 2 2024-05-30, 10:19
Version 1 2017-07-26, 14:57
journal contribution
posted on 2024-05-30, 10:19 authored by G Schofield, KA Katselidis, P Dimopoulos, JD Pantis
We assessed the potential of using natural facial markings to identify individuals in an endangered breeding population of loggerhead sea turtles (Caretta caretta). We divided individual turtles into ten groups based on facial (post-ocular) scale patterns to facilitate rapid comparison of new images in a large photographic catalogue of known turtles (exceeding 400 unique individuals). The matching process was validated by using turtles marked with external flipper tags. An experienced observer achieved a mean 99% success in identifying individuals using photo-id. The reliability and wider utility of the technique was assessed through testing the ability of naïve and trained observers to (1) consistently allocate known (i.e. flipper tagged) individuals into the correct groups (2) correctly match known individuals within one group. In all trials the mean success rate in photographic sorting and matching ranged from 68-100%. A 20 minute training session was found to significantly improve observer ability, i.e the photo-id skills were rapidly acquired by inexperienced workers. Photo-id has the benefit of being suitable for male turtles, which do not come ashore to allow conventional tagging, and so are rarely identified. Photo-id may facilitate the assessment of the numbers of male and female turtles at breeding areas and allow adult sex ratios to be measured. © 2008 Elsevier B.V. All rights reserved.

History

Journal

Journal of experimental marine biology and ecology

Volume

360

Pagination

103-108

ISSN

0022-0981

Publication classification

CN.1 Other journal article

Issue

2

Publisher

Elsevier

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC