• Genetic capture–recapture;
  • Latent multinomial;
  • Mark recapture;
  • Misidentification;
  • Natural tags;
  • Photo-identification


We investigate model inline image for abundance estimation in closed-population capture–recapture studies, where animals are identified from natural marks such as DNA profiles or photographs of distinctive individual features. Model inline image extends the classical model inline image to accommodate errors in identification, by specifying that each sample identification is correct with probability inline image and false with probability inline image. Information about misidentification is gained from a surplus of capture histories with only one entry, which arise from false identifications. We derive an exact closed-form expression for the likelihood for model inline image and show that it can be computed efficiently, in contrast to previous studies which have held the likelihood to be computationally intractable. Our fast computation enables us to conduct a thorough investigation of the statistical properties of the maximum likelihood estimates. We find that the indirect approach to error estimation places high demands on data richness, and good statistical properties in terms of precision and bias require high capture probabilities or many capture occasions. When these requirements are not met, abundance is estimated with very low precision and negative bias, and at the extreme better properties can be obtained by the naive approach of ignoring misidentification error. We recommend that model inline image be used with caution and other strategies for handling misidentification error be considered. We illustrate our study with genetic and photographic surveys of the New Zealand population of southern right whale (Eubalaena australis).