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Estimating Abundance Using Mark–Resight When Sampling Is with Replacement or the Number of Marked Individuals Is Unknown

McClintock, Brett T., White, Gary C., Antolin, Michael F., Tripp, Daniel W.
Biometrics 2009 v.65 no.1 pp. 237-246
Cynomys, biometry, computer software, confidence interval, monitoring, simulation models, Colorado
Although mark–resight methods can often be a less expensive and less invasive means for estimating abundance in long‐term population monitoring programs, two major limitations of the estimators are that they typically require sampling without replacement and/or the number of marked individuals available for resighting to be known exactly. These requirements can often be difficult to achieve. Here we address these limitations by introducing the Poisson log and zero‐truncated Poisson log‐normal mixed effects models (PNE and ZPNE, respectively). The generalized framework of the models allow the efficient use of covariates in modeling resighting rate and individual heterogeneity parameters, information‐theoretic model selection and multimodel inference, and the incorporation of individually unidentified marks. Both models may be implemented using standard statistical computing software, but they have also been added to the mark–recapture freeware package Program MARK. We demonstrate the use and advantages of (Z)PNE using black‐tailed prairie dog data recently collected in Colorado. We also investigate the expected relative performance of the models in simulation experiments. Compared to other available estimators, we generally found (Z)PNE to be more precise with little or no loss in confidence interval coverage. With the recent introduction of the logit‐normal mixed effects model and (Z)PNE, a more flexible and efficient framework for mark–resight abundance estimation is now available for the sampling conditions most commonly encountered in these studies.