A generalized linear mixed model analysis of a multi-vessel fishery resource survey

Helser, Thomas E.; Punt, André E.; Methot, Richard D.

doi:10.1016/j.fishres.2004.08.007

Cited by 56 publications

(59 citation statements)

References 19 publications

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“…Moreover, the random terms do not contribute to the fixed part of the mean, but the variance components associated with them inflate the variability of predictions in an appropriate way (VENABLES and DICHMONT, 2004;CRAWLEY, 2007). The analysis of abundance indices by GLM may underestimate the level of variability since it ignores the grouped nature of tows within vessels (HELSER et al, 2004;BAUM and BLANCHARD, 2010). In this study, the estimations of annual abundance indices from GLM and GLMM were similar as well as the associated variances (Figure 4).…”

Section: Discussion Model Selectionsupporting

confidence: 58%

Standardized catch rate of cockfish, Callorhinchus callorynchus, in a bottom trawl fishery of Patagonia: Is it possible its use as a predictor of abundance trend?

2015

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Standardized catch rate of cockfish, Callorhinchus callorynchus, in a bottom trawl fishery of Patagonia: Is it possible its use as a predictor of abundance trend?The catch per unit of effort (CPUE) data of cockfish, Callorhinchus callorynchus, during 1986-2011 was evaluated for the bottom trawl fishery of the San Matías gulf (Patagonia, Argentina). The objective of this work was to detect what are the factors related to fishery dynamic that affect catch rate of cockfish and to assess standardized CPUE by General linear models (GLMs) and General linear mixed models (GLMMs) as a relative abundance index. The annual trend of the catch rate indicated an increase during the evaluated period. The nominal CPUE and the indices standardized by the Delta-GLM and Delta-GLMM showed the same annual trend, with increases of 57%, 61.1% and 60.7%, respectively. The LogNormal models have the best-fit model and explained 23.5% of the total variability. The factors year, month, depth and hake CPUE explained the highest variability. The analysis of the models indicated that the catch rate of cockfish is subject to the lack of an homogeneous distribution of the fishing effort during different years, months and vessels. This was related to the fleet dynamic searching the targets species. The increasing trend of CPUE and landings would be indicating an increase in effort directionality to the cockfish in the last decade. Descriptors: Standardized CPUE, Holocephali, Chondrichtyans bycatch, GLM, GLMM, Chimaeroids. AbstrActDados de captura por unidade de esforço (CPUE) do peixe-galo Callorhinchus callorynchus foram avaliados para a pesca de arrasto de fundo no Golfo de San Matías (Patagonia, Argentina), durante o período 1986-2011. Os objetivos do trabalho foram identificar quais os fatores relacionados à dinâmica pesqueira que afetam a taxa de captura do peixe-galo e verificar a viabilidade em utilizar dados de CPUEs padronizadas pelos Modelos Lineares Gerais (GLM) e Modelos Lineares Gerais Mistos (GLMMs) como índices de abundância relativa. A tendência anual da taxa de captura indicou um aumento durante o período avaliado. A CPUE nominal e os índices padronizados pelo Delta-GLM e Delta-GLMM apresentaram a mesma tendência anual, com aumentos de 57%, 61.1% e 60.7%, respectivamente. Os modelos LogNormal foram os de melhor ajuste e explicaram 23,5% da variabilidade total dos dados. Ano, mês, profundidade e "hake" CPUE foram os fatores mais explicativos. A análise dos modelos indicou que a taxa de captura do peixe-galo está ligada à falta de distribuição homogênea do esforço de pesca durante os diferentes anos, meses e navios. Este fato esteve relacionado à dinâmica das frotas na busca das espécies alvo. A tendência crescente no aumento das CPUE e descargas em terra estaria indicando a mudança do esforço de pesca direcionado ao peixe-galo na última década.

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Section: Discussion Model Selectionsupporting

confidence: 58%

Standardized catch rate of cockfish, Callorhinchus callorynchus, in a bottom trawl fishery of Patagonia: Is it possible its use as a predictor of abundance trend?

2015

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“…Several previous studies into the standardization of catch and effort data have utilized random effects for vessel and for vessel and year interactions whilst examining fishing power (Bishop et al 2004;Helser et al 2004). Random vessel effects were explored to account for between-vessel variability.…”

Section: Discussionmentioning

confidence: 99%

Defining value per unit effort in mixed métier fisheries

et al. 2015

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Value achieved from time spent at sea is a central driver of fishing decisions and fishing behaviors. Value per unit effort (VPUE) is an important indicator of economic performance in itself and a useful metric within integrated mixed fisheries models. A time series of Irish first sale prices and total per trip landings values (VPT) highlight heterogeneity in fish prices and VPTs achieved by the Irish fleet spatially and temporally, as well as variability with species targeting. This investigation compared models to standardize fishing trip VPUE accounting for species targeting (métier groupings), engine power (a kW proxy for vessel size), seasonal and annual variability, fishing effort, and individual vessels (encompassing variability in vessel characteristics and skipper effects). Linear mixed effects models incorporating random vessel effects and within-group variance between métier groupings performed best at describing the variability in the dataset. All investigated factors were important in explaining variability, and thus important in standardizing VPUE. Models incorporating fishing days (days with reported fishing activity) and engine power as separate 2 variables resulted in improved AIC values. Therefore, fishing days were considered to be the most appropriate effort measure to generate VPUE. The effort unit traditionally applied in measures of per unit effort, fishing hours, performed comparatively poorly in relation to VPT.

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“…En el presente trabajo, en forma semejante a lo propuesto por Helser et al (2004), la ventaja de la modelación estadística de la CPUA es recoger las estimaciones centrales, generando factores que explican su variabilidad y eliminando de alguna manera la incertidumbre generada al aplicar diversos Figura 9. Biomasa (ton) de langostino amarillo estimada mediante MLG por zona y año.…”

Section: Discussionunclassified

“…Modelación estadística y supuestos A menudo, la modelación estadística de las tasas de captura se efectúa mediante un Modelo Lineal Generalizado (MLG) (McCullagh & Nelder, 1989;Gavaris, 1980;Hilborn & Walters, 1992;Punt et al, 2000;Helser et al, 2004), porque el análisis de regresión clásico considera supuestos difíciles de sustentar, que corresponden a la distribución normal de los errores, varianza constante y relación lineal entre la variable respuesta vs variables independientes. La modelación de la CPUA busca no solo identificar y evaluar aquellos factores que explican sus cambios en el tiempo y espacio, y, particularmente en este trabajo, proveer el valor esperado de la densidad a ser empleada en la estimación de la biomasa según el método de "área barrida".…”

Section: Definición De Los Estratosunclassified

Estimacion de la biomasa de langostino amarillo (Cervimunida johni), aplicando Modelo Lineal Generalizado a registros de captura por area barrida en la zona central de Chile

Canales

Arana

2012

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RESUMEN.La información obtenida en cruceros de evaluación directa de langostino amarillo (Cervimunida johni) realizados en la zona central del Chile (32°-38°S) entre 1997 y 2009, permitió modelar los cambios en la densidad anual de este recurso mediante Modelo Lineal Generalizado (MLG). El objetivo de este enfoque fue tanto minimizar el efecto de los distintos criterios y diseños de muestreo que han sido empleados en el tiempo, como también determinar los efectos que explican las variaciones en la densidad de este recurso. Lo anterior permitió estimar el valor esperado de la densidad anual para el cálculo de biomasa mediante el método de "área barrida". Los registros lance a lance de captura por unidad de área barrida (CPUA) permitieron la identificación y medición de los focos de abundancia, además de caracterizar la distribución y área que ha ocupado el recurso en el tiempo. El MLG identificó como efectos significativos el año, la zona, la profundidad, y las interacciones anuales de primer orden, explicando con ello el 43% de la devianza residual del modelo. Los principales efectos que explican las variaciones de la CPUA son el año y la interacción año-zona. Se determinó que la zona con mayor abundancia promedio corresponde a la delimitada entre 32° y 34°S, de igual manera que en el rango de profundidad menor a 250 m. Dentro del período analizado, se determinó que la población de este recurso incrementó en biomasa de manera sostenida hasta el 2006, principalmente en la zona norte del área de estudio, seguida de una fuerte reducción que al 2009 equivale al 75% respecto del máximo, reflejada en una biomasa de 11.000 ton. Palabras clave: evaluación directa, método de área barrida, CPUA, modelo lineal generalizado, langostino amarillo, Cervimunida johni, Chile. Estimating yellow squat lobster (Cervimunida johni) biomass by applying a generalized linear model to catch records per swept area in central ChileABSTRACT. Changes in the annual density of the yellow squat lobster (Cervimunida johni) were modeled using a generalized linear model (GLM), using information obtained during direct stock assessment cruises carried out in central Chile (32°-38°S) between 1997 and 2009. The purpose of this approach was to minimize the effect of different sampling criteria and designs that have been used over time and to determine the factors explain in variations in the density of this resource. The above approach allowed estimating the expected value of the annual density for the biomass calculation through the "swept area" method. By using the haul-to-haul records of catch per unit of the swept area (CPUA), it was possible to identify and measure the points of abundance and characterize the distribution and area occupied by the resource over time. The significant effects identified by the GLM were the year, zone, depth, and first-order annual interactions; which explained 43% of the residual deviance in the model. The main effects that explain the CPUA variations are the year and the year-zone interaction. The zone with the grea...

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A generalized linear mixed model analysis of a multi-vessel fishery resource survey

Cited by 56 publications

References 19 publications

Standardized catch rate of cockfish, Callorhinchus callorynchus, in a bottom trawl fishery of Patagonia: Is it possible its use as a predictor of abundance trend?

Standardized catch rate of cockfish, Callorhinchus callorynchus, in a bottom trawl fishery of Patagonia: Is it possible its use as a predictor of abundance trend?

Defining value per unit effort in mixed métier fisheries

Estimacion de la biomasa de langostino amarillo (Cervimunida johni), aplicando Modelo Lineal Generalizado a registros de captura por area barrida en la zona central de Chile

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