Modelling the growth of tambaqui, Colossoma macropomum (Cuvier, 1816) in floodplain lakes: model selection and multimodel inference

Abstract

The tambaqui, Colossoma macropomum, is one of the most commercially valuable Amazonian fish species, and in the floodplains of the region, they are caught in both rivers and lakes. Most growth studies on this species to date have adjusted only one growth model, the von Bertalanffy, without considering its possible uncertainties. In this study, four different models (von Bertalanffy, Logistic, Gompertz and the general model of Schnüte-Richards) were adjusted to a data set of fish caught within lakes from the middle Solimões River. These models were adjusted by non-linear equations, using the sample size of each age class as its weight. The adjustment evaluation of each model was based on the Akaike Information Criterion (AIC), the variation of AIC between the models (Δi) and the evidence weights (wi). Both the Logistic (Δi = 0.0) and Gompertz (Δi = 1.12) models were supported by the data, but neither of them was clearly superior (wi, respectively 52.44 and 29.95%). Thus, we propose the use of an averaged-model to estimate the asymptotic length (L∞). The averaged-model, based on Logistic and Gompertz models, resulted in an estimate of L∞=90.36, indicating that the tambaqui would take approximately 25 years to reach average size.Keywords: Amazon, Colossoma macropomum, fish growth, model selection, multimodel inference. Modelagem do crescimento do tambaqui (Colossoma macropomumCuvier

Evaluating the most suitable nonlinear growth model for turbot ( Scophthalmus maximus ) in aquaculture 2 (weight application): Multi‐criteria model selection and growth prediction

“…The close‐by results of several of the models in the tested criteria, documents the importance of model evaluation, instead of a priori use of one specific model (Katsanevakis & Maravelias, ). Especially the dominant use of the VBGF for several decades has widely been disputed (Hohendorf, ; Katsanevakis & Maravelias, ; Knight, ; Schnute, ) and several authors have proven other models to be more suitable for different species (Baer et al, ; Costa et al, ; Froese, ; Katsanevakis & Maravelias, ; Wang & Ma, ). Especially datasets composed of many young individuals, as it is mostly the case in aquaculture, are less frequently supported by the VBGF, as it generally supports data of equal ages‐classes or datasets composed of older individuals (Roff, ).…”

“…Most of these functions describe a mathematically fixed sigmoid shaped curve (S‐shape) as it is generally verified to fit the growth data of a variety of fish species (Dumas et al, ). However, the selection of a model is often done arbitrarily or a priori (Baer et al, ; Dumas et al, ), whereby in most cases the original von Bertalanffy growth function (VBGF) is preferred (Baer et al, ; Costa, Barthem, Albernaz, Bittencourt, & Villacorta‐Corrêa, ; Katsanevakis & Maravelias, ). Burnham and Anderson () point out, that the assumption of directly picking the most suitable model out of all models available is highly unlikely, and that even the assumption of one ’true’ model is often not justifiable.…”

Growth curve by Gompertz nonlinear regression model in female and males in tambaqui (Colossoma macropomum)

“…This model is an most appropriate nonlinear equation to describe the tambaqui growth, together with the Logistic model (Costa et al 2013). However, Gompertz model has desirable properties in a growth curve, which unlike other functions, initial and always greater than zero body mass, which reflects the fact that the animal already born with some mass (Fialho 1999).…”

Crecimiento del pez Plagioscion squamosissimus (Perciformes: Sciaenidae) según la inferencia de modelos múltiples en la cuenca del Orinoco medio, Venezuela

“…El modelo más preciso en el ajuste de los datos fue aquel que presentó el valor mínimo del AIC (AIC min ), considerándose que mientras mayor sea la diferencia entre el AIC de cada modelo (AIC c ) con relación al AIC min (ΔAIC c ) menos probable es el modelo de producir un buen ajuste. En cuanto a esto se consideró que los modelos donde el ΔAIC c es menor que dos (ΔAIC c < 2) producen un buen ajuste de los datos, cuando tiene valores entre cuatro y siete (4≤ ΔAIC c ≤ 7) los ajusta medianamente, y cuando es mayor que diez (ΔAIC c > 10) esencialmente no producen un buen ajuste (Costa et al, 2013;Burnham & Anderson, 2002;Arzola-Sotelo, 2013).…”

“…ISSN Impreso: 0034-7744 ISSN electrónico: 2215-2075 provenientes de la selección a priori del modelo de von Bertalanffy (González, Mendoza, Arocha, & Márquez, 2005a), al asumirlo como un modelo verdadero en el ajuste de los datos utilizados, lo cual es una suposición poco realista y no justificada desde el punto de vista matemático (Costa, Barthem, Albernaz, Bittencourt, & Villacorta-Corrêa, 2013). Contrario a la selección a priori del modelo de von Bertalanffy para el estudio del crecimiento de los peces, durante los últimos años se ha considerado como mejor alternativa la inferencia de modelos múltiples de crecimiento y el uso del AIC para seleccionar el de mejor ajuste (Katsanevakis & Maravelias, 2008;Costa et al, 2013;Ansah & Frimpong, 2015;Aragón-Noriega, Alcántara-Razo, Valenzuela-Quiñonez, & Rodriguez-Quiroz, 2015). Un enfoque aplicado en el presente trabajo para validar los resultados obtenidos por González et al (2005a) en cuanto al crecimiento de P. squamosissimus en el Orinoco medio.…”