Simulation Performance of Bayesian Estimators of Abundance Employing Age-at-Harvest and Mark-Recovery Data

The age structure of harvests has long been an important source of information in fisheries stock assessments, especially when augmented with data from catch-effort or research vessel surveys. Age-at-harvest data are also collected for many terrestrial species, a fact which has recently prompted several authors to propose models for analyzing wildlife age-at-harvest data, with the object of estimating abundance, survival, harvest parameters, and recruitment. Since analysis with age-at-harvest data alone often leads to problems with parameter identification, these authors suggested that data from studies of marked animals could be used to inform the estimation of survival and recovery rates. However, little work has been done to examine estimator performance, particularly when model assumptions are violated, as when aging errors occur or when mark-recovery and age-at-harvest data are non-independent. Similarly, we know of no studies that have investigated the efficacy of posterior simulation when Bayesian estimation methods are used for such problems. In this paper, we employ a suite of simulation modules to quantify estimator performance under a number of hypothetical biological scenarios. When all assumptions are satisfied, we show that bias is typically of small magnitude, coefficient of variation is small, and that credible interval coverage is satisfactory. Estimators were robust to errors in age determination but precision had the potential to be severely overestimated when data from marked animals were also included in age-at-harvest summaries. Nevertheless, joint analysis of age-at-harvest and mark-recovery data may represent a viable monitoring strategy for many terrestrial species.