An Index‐Removal Abundance Estimator That Allows for Seasonal Change in Catchability, with Application to Southern Rock Lobster Jasus edwardsii

Abstract

AbstractThe index‐removal method provides estimates of abundance, exploitation rate, and catchability coefficient. Estimates from the original method suffer from poor precision. Recent work has improved the precision of model estimates; however, the method still includes the strong assumption of constant survey catchability over years and seasons. This assumption is not tenable in many fisheries. This work introduces a new multiyear model, 2qIR, that allows catchability to differ between surveys of the same year. Simulations were performed to examine the effects of variability in (1) the exploitation rate among years, (2) survey catchability, and (3) the number of years of data on model performance. The 2qIR model estimates were always more accurate and precise than those of the other models examined and other model scenarios in which there was moderate contrast in exploitation among years, regardless of the seasonal difference between survey catchability coefficients. The ratios of survey catchability tested ranged from 0.1 to 10, but the model worked best at catchability ratios greater than 0.3. The 2qIR model performance improved slightly when a third year was added to the data set, but performance was similar with 3 or 5 years of data. In all types of simulations, the 2qIR model estimates were usable (i.e., not negative, infinite, or made with a convergence error) a greater proportion of the time than were annual model estimates. The 2qIR model produced reasonable results when applied to data from a population of southern rock lobster Jasus edwardsii in Tasmania, whereas the models that assume constant catchability among surveys sometimes predicted exploitation rates exceeding 100%. The results from both the simulations and the lobster data suggest that the 2qIR model can be reliably applied in more situations than models that assume constant survey catchability.