Open AccessPhotographic identification of individuals of a free‐ranging, small terrestrial vertebrate
Author(s) -
Treilibs Claire E.,
Pavey Chris R.,
Hutchinson Mark N.,
Bull C. Michael
Publication year - 2016
Publication title -
ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.17
H-Index - 63
ISSN - 2045-7758
DOI - 10.1002/ece3.1883
Subject(s) - skink , identification (biology) , population , matching (statistics) , ranging , set (abstract data type) , range (aeronautics) , artificial intelligence , computer science , biology , ecology , statistics , demography , mathematics , telecommunications , materials science , sociology , lizard , composite material , programming language
Abstract Recognition of individuals within an animal population is central to a range of estimates about population structure and dynamics. However, traditional methods of distinguishing individuals, by some form of physical marking, often rely on capture and handling which may affect aspects of normal behavior. Photographic identification has been used as a less‐invasive alternative, but limitations in both manual and computer‐automated recognition of individuals are particularly problematic for smaller taxa (<500 g). In this study, we explored the use of photographic identification for individuals of a free‐ranging, small terrestrial reptile using (a) independent observers, and (b) automated matching with the Interactive Individual Identification System (I 3 S Pattern) computer algorithm. We tested the technique on individuals of an Australian skink in the Egernia group, Slater's skink Liopholis slateri , whose natural history and varied scale markings make it a potentially suitable candidate for photo‐identification. From ‘photographic captures’ of skink head profiles, we designed a multi‐choice key based on alternate character states and tested the abilities of observers — with or without experience in wildlife survey — to identify individuals using categorized test photos. We also used the I 3 S Pattern algorithm to match the same set of test photos against a database of 30 individuals. Experienced observers identified a significantly higher proportion of photos correctly (74%) than those with no experience (63%) while the I 3 S software correctly matched 67% as the first ranked match and 83% of images in the top five ranks. This study is one of the first to investigate photo identification with a free‐ranging small vertebrate. The method demonstrated here has the potential to be applied to the developing field of camera‐traps for wildlife survey and thus a wide range of survey and monitoring applications.