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As noninvasive sampling techniques for animal populations have become morepopular, there has been increasing interest in the development ofcapture-recapture models that can accommodate both imperfect detection andmisidentification of individuals (e.g., due to genotyping error). However,current methods do not allow for individual variation in parameters, such asdetection or survival probability. Here we develop misidentification models forcapture-recapture data that can simultaneously account for temporal variation,behavioral effects and individual heterogeneity in parameters. To facilitateBayesian inference using our approach, we extend standard probit regressiontechniques to latent multinomial models where the dimension and zeros of theresponse cannot be observed. We also present a novel Metropolis-Hastings withinGibbs algorithm for fitting these models using Markov chain Monte Carlo. Usingclosed population abundance models for illustration, we re-visit a DNAcapture-recapture population study of black bears in Michigan, USA and findevidence of misidentification due to genotyping error, as well as temporal,behavioral and individual variation in detection probability. We also estimatea salamander population of known size from laboratory experiments evaluatingthe effectiveness of a marking technique commonly used for amphibians and fish.Our model was able to reliably estimate the size of this population andprovided evidence of individual heterogeneity in misidentification probabilitythat is attributable to variable mark quality. Our approach is morecomputationally demanding than previously proposed methods, but it provides theflexibility necessary for a much broader suite of models to be explored whileproperly accounting for uncertainty introduced by misidentification andimperfect detection. In the absence of misidentification, our probitformulation also provides a convenient and efficient Gibbs sampler for Bayesiananalysis of traditional closed population capture-recapture data.

Probit models for capture-recapture data subject to imperfect detection, individual heterogeneity and misidentification