Modelling fish growth: Model selection, multi-model inference and model selection uncertainty

Katsanevakis, Stelios

doi:10.1016/j.fishres.2006.07.002

Cited by 273 publications

(320 citation statements)

References 18 publications

(19 reference statements)

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“…En este estudio, en su lugar se utilizó como criterio la máxima verosimilitud, encontrando que el uso de ésta representa una mejor solución para estimar adecuadamente los parámetros de los modelos de crecimiento individual tal como lo propuso Katsanevakis (2006) para otros modelos, ya en general la máxima verosimilitud es una prueba más robusta.…”

Section: Discussionunclassified

“…La selección de modelos basado en la teoría de la información ha sido recomendado como una alternativa mejor y más robusta que los enfoques tradicionales (Katsanevakis 2006, Cerdenares-Ladrón de Guevara et al 2011. Las ventajas de utilizar el AIC son que los modelos pueden ordenarse jerárquicamente según su ajuste a los datos, y la obtención de su parámetro promedio.…”

Section: Discussionunclassified

“…La selección del modelo basado en la teoría de la información es un paradigma relativamente nuevo en ciencias biológicas, y es muy diferente de los métodos tradicionales basados en probar una hipótesis nula (Schnute & Groot 1992, Burnham & Anderson 2002, Katsanevakis 2006, 2007, Katsanevakis & Maravelias 2008. Bajo este enfoque, el análisis de datos es un proceso integrado que involucra: Seleccionar a priori de un grupo de modelos: Ante la necesidad de ajustar un modelo de forma óptima a un conjunto de datos, es mejor seleccionarlo a partir de un grupo de modelos candidatos (incluidos con base a las características de la especie bajo estudio) en lugar de asumir que existe el mejor modelo y usarlo ajustándolo a los datos.…”

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

Noriega

2013

RBT

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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...

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

Noriega

2013

RBT

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“…Multimodel approaches for describing growth can help reduce model misspecification and can help identify the most appropriate length-at-age model to use for a particular species (Katsanevakis, 2006). Each of the 3 models used were reparameterized versions of commonly used length-at-age models, reformulated to fit the observed change in length information over the time that tagged fish were at large.…”

Section: Growthmentioning

confidence: 99%

Growth, mortality, and movement of cobia (Rachycentron canadum)

Dippold¹,

Leaf²,

Franks³

et al. 2017

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Abstract-We describe the lengthat-age relationship of cobia (Rachycentron canadum) with the use of 3 nonlinear models, and examine both the movement patterns of cobia in the Gulf of Mexico and South Atlantic Ocean and the instantaneous total mortality rate (Z, per year) from tag-recovery models with data from the Sport Fish Tag 0.52-0.61]) was greater than that reported for cobia elsewhere. Movements were reported between the Gulf of Mexico and the South Atlantic Ocean and a statistically significant seasonal trend in recaptures was observed, both of which suggest that cobia inhabit the Florida Keys during the winter and the northcentral Gulf of Mexico during the summer. The most supported tag-recovery model included time-invariant survivorship and time-dependent recovery probability and the estimated Z was 0.59/year (95% CI: 0.55-0.63). This study provides a summary of a long-term cobia tagging program and information for future management of this species.

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“…At-term pups were included in the VIMS1983 data set to account for a lack of neonates within that sample set. Katsanevakis (2006) and Thorson and Simpfendorfer (2009) suggested using multimodel inference to cope with issues of gear selectivity in order to derive more accurate estimates of growth parameters. However, given AIC c values close to or greater than 10, model averaging resulted in parameter estimates that were almost identical to the best-fit model estimates for all data sets.…”

Section: Discussionmentioning

confidence: 99%

Compensatory Growth of the Sandbar Shark in the Western North Atlantic Including the Gulf of Mexico

Romine

Musick²,

Johnson³

2013

Mar Coast Fish

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The number of Sandbar Sharks Carcharhinus plumbeus in the western North Atlantic Ocean has experienced a drastic decline since the early 1980s, reaching a minimum during the early 1990s. Catch rates in the early 1990s were a mere 25% of those during the 1980s. According to several fishery‐independent surveys, the low point in Sandbar Shark abundance followed a period of high exploitation. Growth models fit to age–length data collected from 1980 to 1983 and from 2001 to 2004 were compared to investigate potential changes in parameter estimates that might reveal compensatory responses in the Sandbar Shark population. Statistical differences were found between the model parameters for the two time periods, but the differences in growth rates were minimal. The parameters from the three‐parameter von Bertalanffy growth model for female sharks during the 1980–1983 and 2000–2004 time periods were as follows: L∞ = 188.4 and 178.3 cm FL; k = 0.084 and 0.106; and t 0 = −4.097 and −3.41. For males the growth parameters were as follows: L∞ = 164.63 and 173.66 cm; k = 0.11 and 0.11; and t 0 = −3.62 and −3.33. The estimated age at 50% maturity for female Sandbar Sharks changed from 15 years to 12.49 years between the two time periods. Received December 21, 2012; accepted March 28, 2013

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Modelling fish growth: Model selection, multi-model inference and model selection uncertainty

Cited by 273 publications

References 18 publications

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

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

Growth, mortality, and movement of cobia (Rachycentron canadum)

Compensatory Growth of the Sandbar Shark in the Western North Atlantic Including the Gulf of Mexico

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