Modelación del crecimiento individual del callo de hacha <i>Atrina maura</i> (Bivalvia: Pinnidae) a partir de la inferencia multi modelo

Abstract

Individual growth modeling of the penshell Atrina maura (Bivalvia: Pinnidae) using a multi model inference approach. Growth models of marine animals, for fisheries and/or aquaculture purposes, are based on the popular von Bertalanffy model. This tool is mostly used because its parameters are used to evaluate other fisheries models, such as yield per recruit; nevertheless, there are other alternatives (such as Gompertz, Logistic, Schnute) not yet used by fishery scientists, that may result useful depending on the studied species. The penshell Atrina maura, has been studied for fisheries or aquaculture supplies, but its individual growth has not yet been studied before. The aim of this study was to model the absolute growth of the penshell A. maura using length-age data. For this, five models were assessed to obtain growth parameters: von Bertalanffy, Gompertz, Logistic, Schnute case 1 and Schnute and Richards. The criterion used to select the best models was the Akaike information criterion, as well as the residual squared sum and R 2 adjusted. To get the average asymptotic length, the multi model inference approach was used. According to Akaike information criteria, the Gompertz model better described the absolute growth of A. maura. Following the multi model inference approach the average asymptotic shell length was 218.9mm (IC 212.3-225.5) of shell length. I concluded that the use of the multi model approach and the Akaike information criteria represented the most robust method for growth parameter estimation of A. maura and the von Bertalanffy growth model should not be selected a priori as the true model to obtain the absolute growth in bivalve mollusks like in the studied species in this paper. Rev. Biol. Trop. 61 (3): 1167-1174. Epub 2013 September 01.Key words: multi model inference, Akaike information criteria, Atrina maura, growth models.Los biólogos pesqueros enfrentan varios problemas para describir el crecimiento individual de los organismos sujetos a explotación comercial. Primero, deben seleccionar un modelo que mejor describa los datos de longitud a la edad y para esto tienen una gran cantidad de ellos. Los modelos son llamados por los apellidos de quienes los han propuesto, Bertalanffy, Gompertz, Richards entre otros (Schnute 1981). Una vez que se ha decidido por uno, se deben ajustar los parámetros del modelo a utilizar. Los criterios de ajuste más comunes son la minimización de la suma de los residuales al cuadrado y la obtención de la máxima verosimilitud (Haddon 2001). Posteriormente, si se ha elegido más de un modelo, se debe contar con un criterio para usar el más adecuado. Derivado de lo anterior, surgen tres preguntas: ¿Cuál es el modelo que mejor describe el crecimiento individual de la especie bajo estudio? ¿Cómo elijo el modelo? y ¿Qué pretendo resolver con mi estudio de crecimiento individual?En este caso se decidió modelar el crecimiento individual del callo de hacha Atrina maura (Sowerby 1835). Este molusco bivalvo que habita aguas marinas someras y estuarios en la costa d...

Age, growth, and mortality of Opisthonema libertate on the coasts of northwestern Mexico

“…Information theory (Akaike information criterion) has been used as a selection criterion in multi-model approaches (Cruz-Vázquez et al 2012). This type of approach is based on the parsimony principle: from a set of candidate models, the "best" model is selected considering the relationship between model complexity and model adjustment (Aragón-Noriega 2013). This principle aims to evaluate the biological and statistical plausibility of different models for growth description to maximize the potential of data, as opposed to basing this description on only one model (Cailliet et al 2006).…”

Modelling early growth of the pearl oyster Pteria sterna under pilot‐commercial production

“…Serna‐Gallo et al. (2014) decided to test two asymptotic and one bounded models for P. sterna under culture conditions, while Aragón‐Noriega (2013) tested three non‐asymptotic models to select the best predictive growth curve for the pen‐shell Atrina maura . Lee et al.…”

“…These four models include the versatile model of Schnute that uses parameters to modify the shape of the curve (in this study, the Schnute model was represented by five cases). These equations are all well known, and references can be found in Aragón‐Noriega (2013), Luquín‐Covarrubias et al. (2016), and Zambrano, Aragón‐Noriega, Galindo‐Cortés, Jiménez‐Badillo, and Peralta (2016).…”

“…(2010) and later by Luquín‐Covarrubias, Morales‐Bojórquez, González‐Peláez, and Lluch‐Cota (2016), who named it ‘growth depensation’. Using more than one model to describe growth curves in bivalves is very common nowadays (Aragón‐Noriega, 2013; Aragón‐Noriega, Calderon‐Aguilera, & Pérez‐Valencia, 2015; Luquín‐Covarrubias et al., 2016). The restricted calculation facilities of some years ago permitted only limited comparison between two or more growth models (in comparison with the current situation involving advanced computers).…”

Modelización de la captura y fases de desarrollo de la pesquería de la jaiba azul (Callinectes sapidus) en la Laguna Madre, Tamaulipas, México

“…En su lugar, es la sumatoria de cada β0 y β1 multiplicada por la ponderación (wi %), y con esto se otorga el peso de cada modelo a la estimación de β0 y β1 promedio. Es importante indicar que el uso de R 2 , ajustada o no, y la suma de cuadrados (SRC), no toma en cuenta la estructura del modelo que mejor se ajusta a los datos, sino que la decisión de seleccionarlo se basa en la complejidad del mismo (Aragón-Noriega, 2013). De hecho, R 2 ajustada o no, y la suma de cuadrados, tienden a seleccionar el modelo más complejo (Zhu y col., 2009).…”