Premium
Recommendations for photo‐identification methods used in capture‐recapture models with cetaceans
Author(s) -
Urian Kim,
Gorgone Antoinette,
Read Andrew,
Balmer Brian,
Wells Randall S.,
Berggren Per,
Durban John,
Eguchi Tomoharu,
Rayment William,
Hammond Philip S.
Publication year - 2015
Publication title -
marine mammal science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.723
H-Index - 78
eISSN - 1748-7692
pISSN - 0824-0469
DOI - 10.1111/mms.12141
Subject(s) - optimal distinctiveness theory , mark and recapture , identification (biology) , matching (statistics) , computer science , abundance estimation , selection (genetic algorithm) , set (abstract data type) , abundance (ecology) , visibility , artificial intelligence , statistics , geography , biology , ecology , psychology , mathematics , population , demography , sociology , programming language , meteorology , psychotherapist
Capture‐recapture methods are frequently employed to estimate abundance of cetaceans using photographic techniques and a variety of statistical models. However, there are many unresolved issues regarding the selection and manipulation of images that can potentially impose bias on resulting estimates. To examine the potential impact of these issues we circulated a test data set of dorsal fin images from bottlenose dolphins to several independent research groups. Photo‐identification methods were generally similar, but the selection, scoring, and matching of images varied greatly amongst groups. Based on these results we make the following recommendations. Researchers should: (1) determine the degree of marking, or level of distinctiveness, and use images of sufficient quality to recognize animals of that level of distinctiveness; (2) ensure that markings are sufficiently distinct to eliminate the potential for “twins” to occur; (3) stratify data sets by distinctiveness and generate a series of abundance estimates to investigate the influence of including animals of varying degrees of markings; and (4) strive to examine and incorporate variability among analysts into capture‐recapture estimation. In this paper we summarize these potential sources of bias and provide recommendations for best practices for using natural markings in a capture‐recapture framework.