Abstract
Over the past decade, there has been much methodological development for the estimation of abundance and related demographic parameters using mark-resight data. Often viewed as a less-invasive and less-expensive alternative to conventional mark recapture, mark-resight methods jointly model marked individual encounters and counts of unmarked individuals, and recent extensions accommodate common challenges associated with imperfect detection. When these challenges include both individual detection heterogeneity and an unknown marked sample size, we demonstrate several deficiencies associated with the most widely used mark-resight models currently implemented in the popular capture-recapture freeware Program MARK. We propose a composite likelihood solution based on a zero-inflated Poisson log-normal model and find the performance of this new estimator to be superior in terms of bias and confidence interval coverage. Under Pollock's robust design, we also extend the models to accommodate individual-level random effects across sampling occasions as a potentially more realistic alternative to models that assume independence. As a motivating example, we revisit a previous analysis of mark-resight data for the New Zealand Robin (Petroica australis) and compare inferences from the proposed estimators. For the all-too-common situation where encounter rates are low, individual detection heterogeneity is non-negligible, and the number of marked individuals is unknown, we recommend practitioners use the zero-inflated Poisson log-normal mark-resight estimator as now implemented in Program MARK.