No abstract

We provide a detailed characterisation of the life histories of two commercially important tropical goatfish species, Parupeneus barberinus and Mulloidichthys flavolineatus, from the Commonwealth of the Northern Marianas Islands (CNMI). Two years of continuous fishery-dependent data were used to assess age, growth, mortality and reproduction. Both species are short-lived, with a maximum age of 5 years and maturation within the first year of life. Female and male P. barberinus reach 50% maturity at 15.4- and 20.2-cm fork length (FL) respectively. The M. flavolineatus females and males reach 50% maturity at 15.8- and 16.1-cm FL. Temporal variation in the reproductive cycle of M. flavolineatus indicated that average monthly gonadosomatic index had a clear reproductive period of May–June and an anomalously high peak in November. The reproductive seasonality of P. barberinus was less clear, owing to the abnormally high proportion of inactive physiologically mature females found throughout the year in the fishery across a wide size range. Migrations of P. barberinus into and out of the main fishing area (Saipan lagoon) may explain why the fishery does not encompass the actively spawning population.

Estimating the growth of fishes is critical to understanding their life history and conducting fisheries assessments. It is imperative to sufficiently sample each size and age class of fishes to construct models that accurately reflect biological growth patterns, but this may be a challenging endeavor for highly-exploited species in which older fish are rare. Here, we use the Gulf Corvina (Cynoscion othonopterus), a vulnerable marine fish that has been persistently overfished for two decades, as a model species to compare the performance of several growth models. We fit the von Bertalanffy, Gompertz, logistic, Schnute, and Schnute–Richards growth models to length-at-age data by nonlinear least squares regression and used simple indicators to reveal biased data and ensure our results were biologically feasible. We then explored the consequences of selecting a biased growth model with a per-recruit model that estimated female spawning-stock-biomass-per-recruit and yield-per-recruit. Based on statistics alone, we found that the Schnute–Richards model described our data best. However, it was evident that our data were biased by a bimodal distribution of samples and underrepresentation of large, old individuals, and we found the Schnute–Richards model output to be biologically implausible. By simulating an equal distribution of samples across all age classes, we found that sample distribution distinctly influenced model output for all growth models tested. Consequently, we determined that the growth pattern of the Gulf Corvina was best described by the von Bertalanffy growth model, which was the most robust to biased data, comparable across studies, and statistically comparable to the Schnute–Richards model. Growth model selection had important consequences for assessment, as the per-recruit model employing the Schnute–Richards model fit to raw data predicted the stock to be in a much healthier state than per-recruit models employing other growth models. Our results serve as a reminder of the importance of complete sampling of all size and age classes when possible and transparent identification of biased data when complete sampling is not possible.