Properties of age compositions and mortality estimates derived from cohort slicing of length data

Cohort slicing can be used to obtain catch-at-age data from length frequency distributions when directly measured age data are unavailable. The procedure systematically underestimates the relative abundance of the youngest age groups and overestimates abundance at older ages. Cohort-sliced catch-at-age data can be used to estimate total mortality rate (Z) using a regression estimator or the Chapman-Robson estimator for right truncated data. However, the effect of cohort slicing on accuracy and precision of resulting Z estimates remains to be determined. We used Monte Carlo simulation to estimate the per cent bias and per cent root mean square error of the unweighted regression, weighted regression, and Chapman-Robson mortality estimators applied to cohort-sliced data. Incompletely recruited age groups were truncated from the cohort-sliced catch-at-age data using previously established recommendations and a variety of plus groups was used to combine older age groups. The sensitivity of the results to a range of plausible biological combinations of Z, growth parameters, recruitment variability, and length-at-age error was tested. Our simulation shows that cohort slicing can work well in some cases and poorly in others. Overall, plus group selection was more important in high K scenarios than it was in low K scenarios. Surprisingly, defining the plus group to start at a high age worked well in some cases, although length and age are poorly correlated for old ages. No one estimator was uniformly superior; we therefore provide recommendations concerning the appropriate estimator and plus group to use, depending on the parameters characterizing the stock. We further recommend that simulations be performed to determine exactly which plus group would be most appropriate given the scenario at hand.