The influence of sample distribution on growth model output for a highly-exploited marine fish, the Gulf Corvina (Cynoscion othonopterus)

Abstract

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.

Relationships among somatic growth, climate, and fisheries production in an overexploited marine fish from the Gulf of California, Mexico

“…Several studies have validated the use of annual growth increments in sagittal otoliths as reliable proxies for somatic growth rates in corvina (Bolser et al, 2018; Gherard et al, 2013; Román‐Rodriguez, 2000; Rowell et al, 2005). For this study, we used sagittal otoliths from 457 corvina collected from the commercial fishery at El Golfo de Santa Clara, Sonora, Mexico, during March and April 1997–2016 as part of a long‐term biological and fishery monitoring program for corvina (see Erisman et al, 2014; Román‐Rodriguez, 2000; Rowell et al, 2005).…”

“…For each fish sampled, we also recorded information on total length (TL) to the nearest millimeter. An average of 25 otoliths samples per collection year were processed and analyzed (Table 1), and sampled fish ranged from 2 to 8 years of age based on age assignments of the otolith samples used in previous studies (Bolser et al, 2018; Gherard et al, 2013). No live fish were handled or sacrificed as part of this study, as all otoliths and length measurements were collected from dead fish provided by commercial fishermen participating in the gill net fishery.…”

“…Sagittal otoliths were removed, cleaned, dried whole, and stored until further use. Otoliths were sectioned to 0.5 mm thickness according to the methods established for corvina (Bolser et al, 2018; Gherard et al, 2013) in which they were mounted on wood blocks with cyanoacrylate adhesive and cut through the focus along the dorsal–ventral axis using a double‐bladed Buehler IsoMet 1000 precision saw. Sections were then mounted on a glass slide, polished with lapping film, and viewed under a compound microscope (Zeiss Stemi 2000‐C) using transmitted light and a polarizing filter.…”

“…Measurements were consistently made in the same region of the otolith and along an axis perpendicular to the growth increment boundaries (Figure 2). A complete annulus was defined as one opaque and one translucent zone (Bolser et al, 2018; Gherard et al, 2013). The first increment was measured beginning at the focus of the otolith, and the increment at the edge of the otolith was not measured, because that final annulus was not fully formed.…”

When habitat is lost matters: patterns of population decline and time to extinction in a seasonal, density-dependent model

“…And yet, early models of population growth, such as the logistic growth curve (Verhulst 1845;Pearl and Reed 1920) and the Ricker model (Ricker 1954) did not explicitly incorporate the potential for seasonal dependence, and the population dynamical implications of seasonality are generally underappreciated (White and Hastings 2020). Despite their simplicity, these models can still offer important insights into fundamental ecological processes that underpin the dynamics of a wide range of natural systems (Ricker 1963;Borlestean et al 2015;Romero et al 2017;Bolser et al 2018). Although population models are still frequently framed around a stationary, or 'aseasonal', context (Ludwig 1996;Mueller and Joshi 2000;Lande et al 2003;Otso and Meerson 2010), explicit incorporation of the impacts of seasonality on population dynamics has proven fruitful (Skellam 1967;Fretwell 1972;Kot and Schaffer 1984;Sutherland 1996;Norris 2005;Liz 2017).…”