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Inferring growth for aquatic species is dependent upon accurate descriptions of age-length relationships, which may be degraded by measurement error in observed ages. Ageing error arises from biased and/or imprecise age determinations as a consequence of misinterpretation by readers or inability of ageing structures to accurately reflect true age. A Bayesian errors-in-variables (EIV) approach (i.e., measurement-error modeling) can account for ageing uncertainty during nonlinear growth curve estimation by allowing observed ages to be parametrically modeled as random deviates. Information on the latent age composition then comes from the specified prior distribution, which represents the true age structure of the sampled fish population. In this study, weakfish growth was modeled by means of traditional and measurement-error von Bertalanffy growth curves using otolith- or scale-estimated ages. Age determinations were assumed to be log-normally distributed, thereby incorporating multiplicative error with respect to ageing uncertainty. The prior distribution for true age was assumed to be uniformly distributed between ±4 of the observed age (yr) for each individual. Measurement-error growth models described weakfish that reached larger sizes but at slower rates, with median length-at-age being overestimated by traditional growth curves for the observed age range. In addition, measurement-error models produced slightly narrower credible intervals for parameters of the von Bertalanffy growth function, which may be an artifact of the specified prior distributions. Subjectivity is always apparent in the ageing of fishes and it is recommended that measurement-error growth models be used in conjunction with otolith-estimated ages to accurately capture the age-length relationship that is subsequently used in fisheries stock assessment and management.

A comparison between traditional and measurement-error growth models for weakfishCynoscion regalis