SCRS/2012/038 Collect. Vol. Sci. Pap. ICCAT, 69(1): 210-220 (2013)
AN EXPLORATORY DATA ANALYSIS OF HISTORICAL GROWTH DATA
RECOVERED UNDER THE GBYP
Ana Justel1, Antonio Di Natale1, M’Hamed Idrissi1, Laurence T. Kell1 SUMMARY
Task II length and weight data collected under the ICCAT-GBYP programme were used to analyse the length-weight relationship of the East Atlantic and Mediterranean bluefin tuna stock. This was done in order to identify factors that affect round weight (RWT), to validate these data and to plan subsequent data recovery steps. A GLM model was selected using the glmulti R package, a procedure that proved to be a reliable way of automating the data validation process.
RÉSUMÉ
Les données de longueur-poids de la Tâche II recueillies dans le cadre du programme ICCAT-GBYP ont été utilisées pour analyser la relation longueur-poids du stock de thon rouge de l’Atlantique Est et de la Méditerranée. Cet exercice a été effectué dans le but d'identifier les facteurs qui affectent le poids vif (RWT), de valider ces données et de prévoir les étapes ultérieures de récupération des données. Un modèle GLM a été sélectionné à l'aide du logiciel glmulti R, procédure qui s'est avérée fiable pour automatiser le processus de validation des données.
RESUMEN
Se utilizaron los datos de talla y peso de Tarea II recopilados en el marco del ICCAT-GBYP para analizar las relaciones talla-peso del stock de atún rojo del Atlántico este y Mediterráneo. Esto se realizó con miras a identificar factores que afectan al peso en vivo (RWT), para validar estos datos y para planificar los pasos subsiguientes para la recuperación de datos. Se seleccionó un modelo GLM utilizando un paquete glmulti R, un procedimiento que ha demostrado ser un modo fiable de automatizar el proceso de validación de datos.
KEYWORDS
Bluefin tuna, generalised linear models, automated model selection, GBYP, exploratory data analysis, growth, length, weight
1. Introduction
Changes in body condition due variations in growth and maturity can be caused by a variety of factors e.g. quality or abundance of prey, changes in population density, environmental conditions or differences in migration routes (Tiews, 1963; Roff, 1983). Condition factor can affect reproductive success through reduced fecundity (Hislop et al. 1978) and changes in reproductive schedule (Jorgensen et al. 2006) and shifts in fat and energy stores in relation to reproduction have been documented for the eastern Atlantic bluefin tuna stock (Medina et al. 2002). However, Goldstein et al. 2007 in a study of Atlantic bluefin did not find a significant link between body condition and reproductive state but stated that condition should not be ruled out as a possible factor for determining differences in migration routes or reproductive status.
Biometric studies of length-weight relationships (LWRs) are therefore an important tool for stock assessment (Alot et al., 2011). However, weights-at-age used in the stock assessment for Eastern and Mediterranean bluefin tuna are derived from age slicing of catch-at-size (CAS) data from a variety of fisheries and sampling programmes. This means that variation in condition factor based on working group assessment data reflects changes in the fisheries and measurement error rather than biological processes.
High resolution length and weight data have been recovered under the GBYP and analysing these data will help achieve two of the main objectives set by the Atlantic-wide Research Programme for Bluefin Tuna adopted by the SCRS and the ICCAT Commission (GBYP): i.e. to improve understanding of key biological and ecological processes and to improve assessment models and provision of scientific advice on stock status.
ABFT length-weight relationships have been calculated for different fishing gears and time and size strata (ICCAT Manual, BFT). However due to the wide size range of ABFT and the fact that most of the fisheries are limited to certain months of the year it is difficult to obtain a representative curve for these relationships (Alot et al. 2011). Several studies ((ICCAT Manual, BFT)) have concluded that there is not a single unique BFT growth curve and extensive seasonal changes in the length-weight ratio of bluefin tuna have been found by many investigators working in different areas which collectively represent a considerable part of the coastal habitat of the species (Mather et al. 1995). Seasonal growth patterns have been better documented. Both juvenile and adult grow rapidly during summer and early autumn (up to 10% per month), while growth is negligible in winter. Significant year-to-year and decadal variations in weight-at-age of juveniles in the Western Mediterranean Sea have been also depicted (Fromentin 2003).
2. Materials and methods
2.1 Data
The data used for this work was the database of the historical length and weight data compiled during the GBYP data mining and data recovery programme up to April 2012. The data covers fisheries of BFT from the Bay of Biscay, the Southern Mediterranean Sea, Sardinia, the Strait of Sicily and the Tyrrhenian Sea (see Table 1).
Only observed size and weight samples data were used, excluding any estimates. For that reason, the original GBYP dataset was reduced from 86814 to 42468 records. The BFT specimens analysed ranged in Fork Length (FL) from 35 to 298 cm and from 0’80 to 470 kg in Round Weight (RWT).
2.2 Methods
The data were first explored visually to detect changes in body condition, then a Generalised Linear Model (GLM) was used to identify significant factors affecting condition.
Assuming the allometric equation (Le Cren, 1951) then weight is linearly related to length, i.e.
L
b
+
a
=
(W)
log
Where W is round weight (RW) in kg and L is FL in cm. The condition factor “a” can then be used to evaluate how growth curve varies by year season, fishery and gear. “a” is exponent and “b” is the slope of regression. “b” is often assumed to be equal to 3, i.e. isometry (Tuğrul Zahit Alıçlı,2012).
Based on the results of the exploratory data analysis, it was decided to investigate a Generalized Linear Model (GLM) analysis with the dependent variable RWT for two scenarios: (1) the Tyrrhenian Sea subset and (2) the Strait of Sicily subset.
3. Results and discussion
The results of the exploratory analyses are shown in Figures 1-7. The whole recovered data set is plotted as length-weight curves in Figure 1 by fishery and gear. This shows that the best data coverage is for purse seiners in the Tyrrhenian Sea and longliners and traps in the Strait of Sicily. Therefore these were the data selected for further analysis. Next these three subsets of the data were plotted as mosaic plots by month and year in Figures 2, 3 and 4. Figure 2 shows that for the Tyrrhenian Sea data are only available for three years and that in 2002 the months covered were October and November while in 2003 and 2004 the months covered were May, June and July. Coverage for the Strait of Sicily longliners and traps (Figures 3 and 4) is better; samples mainly come from May and June and cover most of the 2000s.
Length-weight curves for the three subsets are plotted in Figures 5, 6 and 7; colors and lines represent years. This shows that there does appear to be a variation in condition between years.
The main effects variables considered in the GLMs were Length, Year, Month and Gear. The GLMs assumed a normal distribution on the dependent variable and used a link of the log type. These models were obtained through the glmulti R package, a powerful tool for automated model selection (Calcagno et al. 2010). The GLM results can be found in Table 2 and Figures 8-9.
The GML fitted to the Tyrrhenian Sea dataset showed that the most significant explanatory factors included Length, Year, Month and Gear Type; as well as the interaction Month:Year. The model with the best fit overall for the Strait of Sicily dataset included also the interaction Year:Gear (Table 2).
The data analysis carried out in this work was useful not only to understand BFT population dynamics, but also for data validation of the historical task II-SZ data obtained under the GBYP programme.
Acknowledgements
We would like to acknowledge cooperation from all contractors in providing historical data under the ICCAT-GBYP Call for tenders n.2010-2, 2011-1 and 2011-2.
References
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Table 1. EBFT distribution of GBYP DB Task II-SZ data used by fishing ground, fishing gear and year.
Table 2. Summary table of Best models returned by the glmulti() R function for the Tyrrhenian Sea and Strait of Sicily datasets.
DATASET
BEST MODEL (glmulti)
AIC
Tyrrhenian Sea RWT~1+factor(Year)+factor(Month)+factor(Gear)+Length+factor(Month):factor(Year) 40771
Figure 2. Coverage of the Tyrrhenian Sea – Purse Seine dataset by month and year.
Figure 4. Coverage of the Strait of Sicily – Trap dataset by month and year.
Figure 5. Bivariate fit of weight (kg) by length (cm) of the Tyrrhenian Sea – Purse Seine dataset. Colors and lines represent years.
Figure 6. Bivariate fit of weight (kg) by length (cm) of the Strait of Sicily – Longline dataset. Colors and lines represent years.
Figure 7. Bivariate fit of weight (kg) by length (cm) of the Strait of Sicily – Trap dataset. Colors and lines represent years.
Figure 8. GLM diagnostic plots (top) and annual RWT fitted values (bottom) for the Tyrrhenian Sea dataset best model.
Figure 9. GLM diagnostic plots (top) and annual RWT fitted values (bottom) for the Strait of Sicily dataset best mode.
