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Length-based methods are the cornerstone of many population studies and stockassessments. This study tested two widely used methods: the Powell-Wetherall(P-W) plot and the Lmax approach, i.e., estimating Linf directly from Lmax. Inmost simulations, P-W estimates of the ratio total mortality / growth (Z/Kratio) were biased beyond acceptable limits (bias > 30%). Bias in Z/K showed acomplex behavior, without possible corrections. Estimates of asymptotic length(Linf) were less biased than Z/K, but were very sensible to intra-cohortvariability in growth and to changes in the occurrence of large individuals inthe sample. Exclusion of the largest size classes during the regressionprocedure or weighing by abundance does not solve these issues. Perfectlinearization of the data and absurdly narrow confidence intervals for Z/K willlead users to erroneous overconfidence in outputs. Clearly, the P-W method isnot suitable for the assessment of Z/K ratios of natural populations.Estimation of Linf may be tentatively possible under very specific conditions,with necessary external verifications. Also, this study demonstrates that thereis no way to estimate Linf directly from Lmax, since there is no particularrelationship to expect a priori between Linf and Lmax. Errors in estimatingLinf directly affect the estimate of the growth constant K and all othersubsequent calculations in population studies, stock assessments and ecosystemmodels. New approaches are urgently needed for length-based studies of bodygrowth (e.g., unconstrained curve fit with subsequent bootstrapping), thatconsider the inherent uncertainty regarding the underlying data and processes.

How reliable are the Powell-Wetherall plot method and the maximum-length approach? Implications for length-based studies of growth and mortality