The evolution of length-based mortality estimators bridges the gap between data-poor and data-rich situations

The origins of length based methodologies can be traced back to the late 1800’s and the work by C.G .J. Petersen. At the time this novel approach recognized that length frequency distributions, which are generally available, were related to ages and could be used as an assessment tool. It quickly became apparent, however, that the trade off to the limited data requirements was that strong assumptions had to be met to generate reliable results. In the mid-1950’s Beverton and Holt presented their classic length based mortality estimator which detailed the following primary assumptions: 1) Asymptotic growth with known growth parameters that are constant over time 2) No individual variability in growth 3) Constant and continuous recruitment over time 4) Mortality constant with age and, 5) Mortality constant over time. Researchers have struggled with the balance between the appealing aspect of limited data requirements and the restrictions of satisfying these critical assumptions. In this paper we review both the modifications to the basic length-based methodology which relax assumptions for use in the most data-poor situations. We also present recent extensions to length based approaches which can incorporate additional information (e.g. catch rates, multispecies) when available.