Bimodality of the combined removal and signs-of-activities estimator for sampling closed animal populations

The possibility of a bimodal log-likelihood function arises with certain data when the combined removal and signs-of-activities estimator is used. Bimodal log-likelihoods may, in turn, yield disjoint confidence intervals for certain confidence levels. The hypothesis that bimodality is caused by the violation of the equal catchability assumption of the removal model, leading to the combination of contradictory data/models in the combined estimator is set forth. Simulations exploring the effect of the violation of removal model assumptions on estimation and inference showed that the assumption of unequal capture probability influenced the frequency of bimodal likelihoods; similarly, extreme parameter values for probability of capture influenced the number of excessively large confidence intervals produced. A sex-specific combined estimator is developed as a remedial model tailored to the problem. The simulations suggest that both the signs-of-activities estimator and the sex-specific estimator perform equally well over the range of simulations presented, though the signs-of-activities estimator is easier to implement.