The totoaba (Totoaba macdonaldi) is a sciaenid (croaker) fish endemic to the Gulf of California with high commercial importance. Because it was considered at risk of extinction (since 2021 it was reclassified as vulnerable by the IUCN), and aquaculture procedures were developed for restocking and commercial purposes. The present study was conducted with the hypothesis that the early stages of totoaba present depensatory individual growth and an observed variance-at-age modelling approach is the best way to parametrize growth. Ten models were tested including asymptotic, non-asymptotic, exponential-like, and power-like curves including a new one that represents a modification of Schnute’s model. The model that best described the growth trajectory of larval and early juveniles of T. macdonaldi in a controlled environment is a sigmoid curve with two inflexions, related to changes in the feeding regime.

Obtaining the best possible estimates of individual growth parameters is essential in studies of physiology, fisheries management, and conservation of natural resources since growth is a key component of population dynamics. In the present work, we use data of an endangered fish species to demonstrate the importance of selecting the right data error structure when fitting growth models in multimodel inference. The totoaba (Totoaba macdonaldi) is a fish species endemic to the Gulf of California increasingly studied in recent times due to a perceived threat of extinction. Previous works estimated individual growth using the von Bertalanffy model assuming a constant variance of length-at-age. Here, we reanalyze the same data under five different variance assumptions to fit the von Bertalanffy and Gompertz models. We found consistent significant differences between the constant and nonconstant error structure scenarios and provide an example of the consequences using the growth performance index ϕ′ to show how using the wrong error structure can produce growth parameter values that can lead to biased conclusions. Based on these results, for totoaba and other related species, we recommend using the observed error structure to obtain the individual growth parameters.

Modeling individual growth in marine species for aquaculture encounters many difficulties when the species pauses its growth but resumes its later after the disrupting phenomenon (environmental or physiological) has been overcome. Seasonal or oscillatory growth has been addressed by modifying existing models, such as von Bertalanffy and Gompertz, to include an oscillatory component in this study. The novelty of this study lies in the fractal approach used to analyze growth using multiple logistic functions. Three commercially farmed marine species were studied, including shellfish, crustacea, and finfish. The oscillatory version of the von Bertalanffy model as well as double and triple logistic functions were used for analysis. The best model was selected using the information theory, Specifically the Akaike criterion (AIC) and the Bayesian criterion (BIC). Normal and log-normal distributions of error were assumed. The triple logistic model with log-normal distribution in the error structure was found to be the best model to describe the growth pattern of the three commercially farmed species as it obtained the lowest AIC. Overall, this study concludes that the fractal approach is the most effective way to describe growth in farmed species, including shellfish, crustacean, and finfish.

El presente estudio evaluó la bioacumulación de cobre (Cu) y zinc (Zn) en el ostión japonés (Crassostrea gigas) cultivado en el área contigua al Instituto Tecnológico de Guaymas, en la bahía de Guaymas, Sonora, México, durante enero–junio de 2023. Al inicio del estudio, los organismos tenían una talla promedio de 64.566 ± 7.03 mm y un peso de 19.560 ± 4.69 g. Se analizaron mensualmente variables biométricas y concentraciones tisulares de metales para determinar su relación con el tiempo de cultivo. Los organismos mostraron crecimiento somático sostenido, con tasas promedio de 0.253 mm/día en talla y 0.491 g/día en peso. Las correlaciones de Pearson evidenciaron relación positiva fuerte entre el tiempo y la talla (r = 0.841) y el peso (r = 0.934), así como entre talla y peso (r = 0.956). La concentración media de Cu fue de 108.46 ± 72.66 mg/kg y la de Zn de 469.59 ± 381.80 mg/kg, con variación temporal significativa. Sin embargo, ambas mostraron correlaciones negativas con el tiempo (Zn: r = −0.849; Cu: r = −0.490), indicando tendencia decreciente. Los resultados no confirmaron acumulación progresiva y sugieren posible efecto de dilución biológica asociado al incremento de biomasa y regulación fisiológica

Growth is the most influential aspect in demographic species analysis. Collecting data on ages and sizes (such as length and weight) is a fundamental step in growth modeling, particularly in fishery science. Residual analysis plays a crucial role in parameterizing the mathematical models chosen to describe the growth patterns of the species under investigation. Using optimal residual criteria is essential to improving model performance and accuracy. In the present study, the length-at-age data of the Gulf corvina (Cynoscion othonopterus) were evaluated with the Schnute model to obtain the best error type and to establish the most accurate growth pattern. Later, the observed, constant, depensatory, and compensatory variance approaches were tested using the logistic model. The Bayesian information criterion (BIC) was used as the goodness-of-fit test to obtain the best variance approach parametrizing the growth model. The BIC values selected the observed variance as the best approach to parametrize the logistic growth model. The conclusion is that the observed variance approach produces robust results—that is, the observed variance produced the most plausible fits. It is suggested that the observed error structure should be used to estimate individual growth.