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Estimating the proportion of identifiable individuals and group sizes in photographic identification studies
Author(s) -
Eguchi Tomoharu
Publication year - 2014
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.12119
Subject(s) - statistics , sampling (signal processing) , covariate , inference , group (periodic table) , bayesian probability , mathematics , statistical inference , posterior probability , bayesian inference , population , artificial intelligence , pattern recognition (psychology) , computer science , demography , computer vision , chemistry , organic chemistry , filter (signal processing) , sociology
Simple Bayesian statistical models are introduced to estimate the proportion of identifiable individuals and group sizes in photographic identification, or photo‐ ID , studies of animals that are found in groups. The models require a simple random photographic sampling of animals, where the photographic captures are treated as sampling with replacement within each group. The total number of images, including those that cannot be identified, and the number of images that contain identifiable individuals are used to make inference about the proportion of identifiable individuals within each group and as the population when a number of groups are sampled. The numbers of images for individuals within each group are used to make inference about the group size. Based on analyses of simulated and real data, the models perform well with respect to accuracy and precision of posterior distributions of the parameters. Widths of posterior intervals were affected by the number of groups sampled, sampling duration, and the proportion of identifiable individuals in each group that was sampled. The structure of the models can accommodate covariates, which may affect photographic efficiency, defined in this study as the probability of photographically capturing individuals.