1 Updated Standardized CPUE of Atlantic Bluefin Tuna Caught

SCRS/08/100

Updated standardized CPUE of Atlantic bluefin tuna caught by the Spanish baitboat fishery in the Bay of Biscay (Eastern Atlantic). Time series from 1975 to 2007.

Enrique Rodriguez-Marin1, Mauricio Ortiz2, Cristina Rodríguez-Cabello1 and Santiago Barreiro1

SUMMARY Updated standardized relative abundance indices by age are presented for bluefin tuna baitboat fishery in the Bay of Biscay from 1975 to 2007. Standardization was carried out using generalized linear mixed models. Catch and effort data on bluefin tuna were available from catches by trip; this catch by commercial category was converted to catch at age by applying seasonal age length keys to the length distribution by commercial category. In this update the age length keys of summer of the entire study period were reviewed. Age class was included as a fixed factor within model specifications because the fishery operates on all available stock fractions, and age determination of the tuna landings is done after removal of the catch. Splitting of the catches into age classes introduces a large number of zero values, so data were modeled using the delta- lognormal model. The model finally selected included the following explanatory factors: Year, Age, Month and Year × Age fixed factors, plus a selection of other factors contributing with a significant percentage of the total explained deviance in the aggregated model. All Year interactions besides the Year × Age factor were considered as random. CVs of present standardized index are less variable than the previous standardization time series from 1975 to 2004, but still some variability was found for the last years when the vessels built more recently and larger are included in the analysis. The revised age length keys seem to be reducing CVs variability in the study period. The standardized index of ages 2, 3 and 4 show a downward tendency in the last years.

RESUMEN Se presenta la actualización del índice de abundancia relativa por edad de atún rojo de la pesquería de cebo vivo del Golfo de Vizcaya desde 1975 hasta 2007. La estandarización se realizó utilizando modelos lineales mixtos generalizados. Se utilizaron datos de captura y esfuerzo de atún rojo procedentes de capturas por marea; esta captura por categoría comercial fue convertida en captura por edad, aplicando claves talla edad estacionales a las distribuciones de talla de cada categoría comercial. En esta actualización se han revisado las claves talla edad de verano de todo el periodo de estudio. La edad fue incluida como un factor fijo en el modelo puesto que la pesquería captura toda la población disponible y la determinación de la edad se realiza a posteriori. Al dividir las capturas en edades se introduce un elevado número de ceros, por lo que los datos se modelaron utilizando el modelo delta-lognormal. El modelo final seleccionado incluye los siguientes factores explicativos: año, edad, mes y la interacción fija año × edad, más una selección de factores que contribuyen a explicar la variabilidad del modelo. Todas las interacciones consideradas después de la interacción fija año × edad fueron consideradas como aleatorias. Los CVs del índice actualizado son menos variables que los obtenidos en la pasada estandarización (1975-2004), pero todavía hay un incremento en los mismos a partir de la inclusión en el índice de nuevos barcos más grandes y construidos recientemente. La revisión realizada el las claves talla-edad reducen la variabilidad en los CVs. Los índices estandarizados de las edades 2,3 y 4 muestran una tendencia descendente en los últimos años.

KEYWORDS ABFT, catch per unit effort, generalized linear models, standardization.

1 Instituto Español de Oceanografía. Apdo. 240. 39080 Santander. Spain. [email protected] 2 National Marine Fisheries Service (NMFS), Southeast Fisheries Science Center. Miami Laboratory. United States. 1 Introduction At the end of spring, Atlantic bluefin tuna (Thunnus thynnus) (ABFT) reaches the southeast corner of the Bay of Biscay and remains till autumn. The geographical distribution of catches coincides with the accumulation of temperate waters occurring annually in this area of the Bay (Figure 1). ABFT migrates to the Bay of Biscay for feeding, since the influx of continental water from French rivers together with coastal upwelling processes and the formation of anticyclonic gyres in the Cantabrian Sea gives rise to high productivity values. Small pelagic species such as anchovy, sardine, horse mackerel and mackerel take advantage of the phyto- and zooplankton bloom, which takes place in spring and the start of summer, for spawning. Pelagic crustaceans and the recruits of these pelagic species, born in the same year or in the previous spring, constitute the reason for the ABFT trophic migration. The ABFT that migrate to the Bay of Biscay are juveniles, mainly of 1 to 5 years. The most abundant ages in catches are specimens of 2 and 1 years (in 2007 the regulations changed regarding minimum length and age 1 can no longer be caught). Albacore (Thunnus alalunga) also migrate towards the Bay of Biscay in summer, but they reach the Bay a little later than ABFT and although their distributions overlap, most of the Albacore catches take place outside the warm water area, to the west of 4ºW. Both species leave the Bay of Biscay in mid-autumn.

Catches targeting ABFT are made exclusively by baitboat vessels operating from the easternmost ports of the Cantabrian Sea, Hondarribia and Getaria (Figure 1). The latter began to target ABFT in 1996. Catches landed at both ports now make up 90 % of ABFT catches in the Bay of Biscay (although some vessels from nearby ports, such as Ondarroa and Bermeo, also sporadically target ABFT). The remainder is from incidental troll and baitboat fishing for albacore in the Bay of Biscay.

This paper updates the previous standardized ABFT cpue index by age-class for the baitboat fishery of the Bay of Biscay (Rodriguez-Marin et al., 2003; Rodriguez-Marin et al., 2007).

Material and methods

The same methodology detailed in Rodriguez-Marin et al. (2003) was followed. Catch and effort data were collected from the ports of Hondarribia and Getaria between 1975 and 2007. Data were obtained from landing returns, interviews and logbooks. The information collected for each trip included: name of the vessel, date of landing, number of ABFT by commercial category and number of fishing days. Catch by commercial category was converted to catch at age by applying seasonal age length keys to the length distribution by commercial category. This methodology was employed since changes took place in commercial categories during the study period and new ones were established in 2008 ( Table 1). Direct ageing was estimated from spine sections interpretation. Ages 5 and older were combined as 5+, since ages above are scarcely represented in the fishery for the period of study. In this update the age length keys of summer of the entire study period were reviewed.

Vessel characteristics were assembled using information from official fleet directories and personal interviews with skippers. The information by boat and year includes: the name of the vessel, its home port, the year it was built, horsepower (HP), gross registered tonnage (GRT), length, construction material, crew number, number of bait tanks and name of the skipper, the presence or absence of radio direction finder, and number of the following devices: navigation radar, GPS, plotter, colour echo- sounder, monochrome sonar and colour sonar. In order to reduce the number of variables related to type of vessel to be included in the model, analyses were done for the reduction of these variables (described in Rodriguez-Marin et al., 2002). The variables GRT, HP and length were used to define three types of vessels. A criterion of baitboats with five or more years in the catch history of the time- series was used for data selection.

Nominal cpue was calculated as the number of fish of a particular age caught by day of fishing trip. The number of days that are counted as effort in a fishing trip of one vessel exclusively catching ABFT is 100%. Nevertheless, if the vessel catches several species, albacore or less often big-eye tuna (Thunnus obesus), the assignation of effort is made taking into account the proportion in number of ABFT with respect to the remaining species and their size: 1. If ABFT catches are less than 5% and 2 smaller than 7 kg, the fishing trip is discarded. 2. If ABFT represents values of less than 5% but they are tunas greater than 7 kg and if they represent values between 5 and 15% independently of size, one third of the effort is assigned. 3. If ABFT represents between 15 and 50% half of the effort is assigned. 4. If it is greater than 50%, then all the effort is assigned. This criterion was applied because tuna fishing requires time for searching, and because they are taken one by one and the large ABFT are harder to catch, they need a specific fishing technique. There is a difference in fishing areas between the vessels targeting albacore and those targeting ABFT and there are also differences in fishing gear and in the live bait (Rodríguez-Marín et al., 2005).

Relative abundance indices for ABFT by age-class were estimated using generalised linear mixed models (GLMM). Age class was included as a fixed factor within model specifications, with the Year * Age interaction as a fixed factor component, in order to obtain yearly indices by age. This model specification was selected against standardization models for each age class independently, because the fishery operates on all available stock fractions, and age determination of the tuna landings is done after removal of the catch. The proportion of positive cpue to total number of trips, grouped by year, age and month, was high for all ages 1 to 5+, and ages 2 and 3 made up the highest values (Figure 2). A delta lognormal-binomial model was applied to obtain the standardized index, in which the proportion of positive trips fits separately assuming a binomial error distribution, while non-zero catch rates are modelled assuming a lognormal error distribution (Lo et al., 1992). The standardised index is the product of these two model-estimated components. Lognormal transformed frequency distributions of positive trips for each age class of ABFT are shown in Figure 3.

Relative indices from the Delta model formulations were calculated as the product of the Year*Age effect least square means (LSmeans) from the binomial and the lognormal model components. The lognormal LSmeans estimates used a weighted factor proportional to the number of observations in the input data, to account for the unbalanced nature of the data. In addition, a bias correction was applied to the lognormal estimates using the algorithm described by Lo et al. (1992). Analysis and model formulations for the Delta model were done using the Glimmix and Mixed procedures from the SAS® statistical software package (SAS Institute 1997). In general model evaluation and diagnoses were done using residual analysis (McCullagh and Nelder, 1989). For the Delta model, diagnostic plots are presented for the lognormal component: QQ-plots against year are presented for each age class.

A step-wise regression procedure was used to determine the set of systematic factors and interactions that significantly explained the observed variability in each model. A Chi-square test was used to evaluate the statistical significance of an additional factor (McCullagh and Nelder, 1989). In addition, the corresponding percentage of deviance explained by each factor relative to the maximum model may be estimated to get a profile of the most important explanatory factors in the model. A statistically significant variable may in some instances be better left out of the model if the amount of variation explained by the variable is small in relation to the complexity that it adds (Stefánsson, 1996).

The final model included Year, Age, Month and Year × Age fixed factors, plus a selection of other explanatory factors that explained a significant percentage of the total deviance in the aggregated model. All Year interactions besides the Year × Age factor were considered as random. The significance of random interaction was evaluated using the Akaike information criterion (AIC), the Schwarz Bayesina criterion, and the Chi-squared test of the deviance of the -2 log-likelihood statistic between successive model formulations (Little et al., 1996)

Results

Trip database In the study period of 33 years, 9 744 fishing trips were analyzed, which practically form a census of ABFT landings made by the fleet targeting this species in the Bay of Biscay. The fishing days corresponding to these trips are spread between the months of June and November, and July and August bear 60 % of the monthly mean of fishing days in the period analyzed. The fishing seasons last

3 five months on average. Vessels make a mean of 10 fishing trips per year with a mean duration of 3 days, and there is a slightly falling trend in the number of days of their duration.

The number of vessels remained constant at around 25 units until 1996, when boats from the port of Getaria began to join the fishery in varying numbers. From 1996 to the present the mean number of baitboat vessels targeting ABFT has been 37, although the number of vessels in Hondarribia has fallen over the last years, Figure 4Figure 4. The vessels of Hondarribia are generally smaller than those of Getaria: the mean values for the 1996-2007 period are, for Hondarribia, length: 26 m, HP: 770 and GRT: 110; and for Getaria, length: 30 m, HP: 965 and GRT: 150.

The ages that mostly appear in the catches by fishing trip are, in order of importance, ages 2, 3 and 1, although the most abundant in number, reaching more than 90 % of the total, are ages 2, 1 and 3, with age 2 the most abundant at 40%. Catches at age throughout the months of the fishing season are shown in Figure 5, which shows that the frequency in number of all ages is greater in the first half of the fishing season, except for age 1, which is more abundant in the catches made in autumn.

Analysis of Catch rate. The results of deviance analysis are shown in Table 2. For the positive catch rates, Year, Age, Month and Year × Age are clearly the main explanatory factors, but Year × Month interaction is also significant. For the proportion of positive trips, the main explanatory factors were Year, Age and Month. The selected final model included the following explanatory factors: Year, Age, Month, Crew, No.Tanks, Boat Type, Year × Age with Year × Month, Year × No.Tanks, Year × Boat Type interactions as random components. Random effects were tested for significance. Table 4 show the results from the random components analyses used for final model selection. The inclusion of interactions represented an improvement over the models without these interactions.

Observed and standardized scaled to the mean cpue series by age are shown in Figure 6, and Figure 7 shows standardized indices and corresponding coefficients of variation (CVs) of the previous (Rodríguez-Marín et al., 2007) and present standardization. The residuals follow the expected linear pattern for the positive catch rates in the QQ-plots (Figure 8). The table with the nominal and standardized cpue by age-class appears in the Appendix.

Discussion The fishing method does not show great change over the whole historical series, except for the evolution of technology, but technological innovation has not been shown to be significant, almost certainly because these changes were quickly implemented by the fleet over a short time, thus producing very little contrast in the annual trend in catch and effort data (Rodriguez-Marin et al., 2003). The introduction of monochrome sonar took place just at the start of the data series of this study, and so its influence on fishing power, described by Cort and Bard (1980), is not reflected in the overall trend. The only difference to appear recently regarding the fishing method is the introduction of hydraulic or electric reels to pull the cable supported at the top of the long rods used for ABFT of over 10 kg. These reels began to appear on vessels of the baitboat fleet in 2002, but despite the help they provide in lifting the tuna aboard they have not had any influence on the crew number, since the same size crew continues to be necessary to handle the rods. Other technological advances which have occurred in recent years are the use of hydraulic arms for purse-seine nets, fish-sucking vacuum pumps for loading the live bait tanks directly from the purse-seine nets, and echo sounders with a wider detection capacity.

Age and month are the most important factors, explaining most of the variability in the final model. The possible causes of the influence of age and month on catch rates were discussed in Rodríguez- Marín et al. (2003), among which are recruitment to the fishery, different migratory patterns by age and in relation to sexual maturity, different temporal appearance in the fishing area in relation to weather conditions and schooling behaviour by age.

4 There are another three factors - crew number, vessel type and number of bait tanks - which, although they do not explain a significant amount of the variability of the model, do explain a small percentage. Crew size is related to the number of rods and the size of the tuna that can be caught. Vessel type concerns size and more powerful engines, which affords greater autonomy and increased capacity for search and movement to areas where bluefin tuna are being caught at a given time. The number of bait tanks is related to the greater or smaller capacity to fish without having to return to coastal areas to get more live bait, thus saving the time that such a manoeuvre would take. Total bait tank capacity is a better measurement of fishing efficiency than number of bait tanks and should be explored in the future. At present bait tanks are much bigger than in the past, as for example the new boats that have the greatest tank capacity, 100 000 l, have only 9 tanks.

CVs of present standardized index are less variable than the previous standardization time series from 1975 to 2004 (Rodríguez-Marín et al., 2007) but still some variability is found for the last years when the vessels built more recently and larger from Getaria are included in the analysis. The revised age length keys seem to be reducing CVs variability in the study period. The standardized index of ages 2, 3 and 4 show a downward tendency in the last years.

Acknowledgements This study is the continuation of a project financed by the European Commission (DG XIV), IEO, AZTI, University of Azores and CEFAS.

References Cort, J. L., and F.X. Bard. 1980. Descripción de la pesquería de atún rojo (Thunnus thynnus) del Golfo de Vizcaya. Collective Volume of Scientific Papers, ICCAT, 11: 390–395.

Littell, R. C., G. A. Milliken, W. W. Stroup and R. D. Wolfinger. 1996. SAS_ System for Mixed Models. SAS Institute Inc., Cary, NC. 663 pp.

Lo, N.C., Jacobson, L.D. and Squire, J.L. 1992. Indices of relative abundance from fish spotter data based on Delta-lognormal models. Canadian Journal of Fisheries and Aquatic Sciences, 49: 2515- 2526.

McCULLAGH, P. and J.A. Nelder, 1989. Generalized Linear Models. 2nd edition. Chapman and Hall, London. 509 pp.

Rodríguez-Marín, E., H. Arrizabalaga, M. Ortiz, C. Rodríguez-Cabello, G. Moreno and L. T. Kell. 2003. Standardization of bluefin tuna, Thunnus thynnus, catch per unit effort in the baitboat fishery of the Bay of Biscay (Eastern Atlantic). ICES Journal of Marine Science, 60(6): 1215-1230.

Rodríguez-Marín, E., G. Moreno, C. Rodríguez-Cabello, M. Ortiz and H. Arrizabalaga. 2002. Description and evolution of the baitboat fleet targeting bluefin tuna in the Bay of Biscay from 1975 to 2000. Collective Volume of Scientific Papers, ICCAT, 54(2): 561–573.

Rodríguez-Marín E., C. Rodríguez-Cabello, S. Barreiro and J.L. Cort. 2005. Description of Bluefin Tuna Targeted and Non-targeted Fisheries in the Bay of Biscay from 1990 to the Present. Collective Volume of Scientific Papers, ICCAT, 58(2): 783-790.

Rodríguez-Marín, E., M. Soto, M. Ortiz, C. Rodríguez-Cabello and J.L. Cort. 2007. Updated Standardization of bluefin tuna, Thunnus thynnus, Catch per Unit of Effort in the baitboat fishery of the Bay of Biscay (Eastern Atlantic). Time series from 1975 to 2004. Collective Volume of Scientific Papers, ICCAT, 60(4): 1237-1249.

SAS Institute Inc. 1997, SAS/STAT® Software: Changes and Enhancements through Release 6.12. SAS Institute Inc., Cary, NC. 1167 pp. 5

Stefánsson, G. 1996. Analysis of groundfish survey abundance data: combining the GLM and delta approaches. ICES Journal of Marine Science, 53: 577–588.

Table 1. Changes in commercial category during the study period 1975-1989 1990-1993 1994-2007 2008 < 7 kg < 7 kg < 10 kg 8 - 15 kg 7 - 15 kg 7 - 15 kg 10 - 25 kg 15 - 25 kg 15 - 25 kg 15 - 30 kg 25 - 35 kg 25 - 50 kg 25 - 50 kg 30 - 50 kg 35 - 50 kg 50 - 100 kg 50 - 100 kg 50 - 100 kg 50 - 100 kg >100 kg >100 kg >100 kg >100 kg

Table 2a. Deviance table for ABFT catch rates of positive trips without including the Age as factor in the model evaluation. Residual Change in % of total Model factors for positive catch rates values d.f. p deviance deviance deviance 1 1 17292.428 Year 32 14605.909 2686.52 50.0% < 0.001 + Month 5 14284.502 321.41 6.0% < 0.001 + Home port 1 14265.284 19.22 0.4% < 0.001 + Construc.material 2 14209.662 55.62 1.0% < 0.001 + No. Tanks 8 14077.746 131.92 2.5% < 0.001 + C. echo-sounder 2 14062.267 15.48 0.3% < 0.001 + M. sonar 3 14042.500 19.77 0.4% < 0.001 + C. sonar 3 14027.005 15.50 0.3% 0.001 + Nav. radar 2 14024.936 2.07 0.0% 0.355 + GPS 3 13997.286 27.65 0.5% < 0.001 + Boat type 2 13970.278 27.01 0.5% < 0.001 + Year*Construct. material 18 13945.099 25.18 0.5% 0.120 Or + Year*C. sonar 36 13928.427 41.85 0.8% 0.232 Or + Year*Nav. Radar 43 13897.105 73.17 1.4% 0.003 Or + Year*C. echo-sounder 28 13888.162 82.12 1.5% < 0.001 Or + Year*M. sonar 44 13887.484 82.79 1.5% < 0.001 Or + Year*Home port 11 13874.688 95.59 1.8% < 0.001 Or + Year*Boat type 54 13856.433 113.85 2.1% < 0.001 Or + Year*GPS 37 13806.073 164.21 3.1% < 0.001 Or + Year*Plotter 16 13661.264 309.01 5.7% < 0.001 Or + Year*No. Tanks 159 13644.673 325.61 6.1% < 0.001 Or + Year*Radio direc. finder 33 12590.841 1379.44 25.7% < 0.001 Or + Year*Month 130 11916.627 2053.65 38.2% < 0.001

6 Table 3b. Deviance table for ABFT catch rates of positive trips and proportion of positive/total trips from the baitboat fishery including the Age as factor in the model evaluation. Residual Change in % of total Model factors for positive catch rates values d.f. p deviance deviance deviance 1 1 261337.384 Year 32 250484.781 10852.60 9.9% < 0.001 + Age 4 212186.138 38298.64 34.9% < 0.001 + Month 5 210170.308 2015.83 1.8% < 0.001 + Crew 2 209618.613 551.69 0.5% < 0.001 + No. Tanks 3 209503.428 115.18 0.1% < 0.001 + Boat type 2 209256.301 247.13 0.2% < 0.001 + Year*Age 127 159507.303 49749.00 45.3% < 0.001 + Crew*No. Tanks 2 159490.378 16.93 0.0% < 0.001 Or + Crew*Boat type 2 159464.027 43.28 0.0% < 0.001 Or + Month*Crew 10 159324.176 183.13 0.2% < 0.001 Or + Year*Crew 46 159271.735 235.57 0.2% < 0.001 Or + Month*Boat type 10 159209.793 297.51 0.3% < 0.001 Or + Month*No. Tanks 15 159109.327 397.98 0.4% < 0.001 Or + Year*Boat type 54 159041.35 465.95 0.4% < 0.001 Or + Year*No. Tanks 56 159015.047 492.26 0.4% < 0.001 Or + Year*Month 130 151599.467 7907.84 7.2% < 0.001 Proportion of positive/total trips 1 1 26488.762 Year 32 24153.056 2335.71 13% < 0.001 + Age 4 19491.355 4661.70 26% < 0.001 + Month 5 16838.668 2652.69 15% < 0.001 + Crew 2 16838.416 0.25 0% 0.882 + No. Tanks 3 16834.745 3.67 0% 0.299 + Boat type 2 16809.657 25.09 0% < 0.001 + Year*Age 128 10979.734 5829.92 33% < 0.001 + Crew*No. Tanks 2 10979.501 0.23 0% 0.890 Or + Crew*Boat type 2 10976.843 2.89 0% 0.236 Or + Month*Crew 10 10930.117 49.62 0% < 0.001 Or + Year*Crew 46 10902.008 77.73 0% 0.002 Or + Year*Boat type 54 10880.514 99.22 1% < 0.001 Or + Year*No. Tanks 56 10873.049 106.68 1% < 0.001 Or + Month*Boat type 10 10838.044 141.69 1% < 0.001 Or + Month*No. Tanks 15 10833.355 146.38 1% < 0.001 Or + Year*Month 130 8780.302 2199.43 12% < 0.001

Table 4. Random effects test for significance of successive model formulations with random factors (in italics). Residual Akaike's Schwartz's Likelihood ABFT GLMixed Model log- Information Bayesian Ratio Test likelihood Criterion Criterion Positives catch rates Year Age Month Crew No.Tanks Boat Type Year*Age 153876 153878 153887 Year Age Month Crew No.Tanks Boat Type Year*Age + Year*Month 152558 152562 152568 1318 0.000 Year Age Month Crew No.Tanks Boat Type Year*Age + Year*Month Year*No.Tanks 152547 152553 152562 11 0.001 Year Age Month Crew No.Tanks Boat Type Year*Age + Year*Month Year*No.Tanks Year*Boat Type 152536.2 152544 152557 10.8 0.001 Proportion Positives Year Age Month Year*Age 3341.8 3343.8 3348.4 Year Age Month Year*Age Year*Month 3292.7 3296.7 3202.9 49.1 0.000 Year Age Month Year*Age Year*Month Age*Month 3235.6 3241.6 3251 57.1 0.000 7 50 N 49 Area enlarged 48 5 00 m 47

Ho Ge nd 43 tar arr ia ibia SPAIN 42 11109876543210W

Figure 1. Top: SST images from 2006 months from June to August in the Bay of Biscay (produced by Remote Sensing IEO Santander). Bottom: The study area, and ABFT fishing locations based on logbook data (from Rodríguez-Marín et al. 2003). 8

1.0 positive 0.8 zero

0.5 Proportion 0.3

0.0 Age1 Age2 Age3 Age4 Age5+

Figure 2. Proportion of zero and positive baitboat trips for ABFT by age class.

Age5+ 0.25 0.20 0.15 0.10 0.05 0.00 Age3 Age4 0.25 0.20 0.15 0.10 0.05 0.00 Age1 Age2 0.25 0.20 0.15 0.10 0.05 0.00 -12.6 -9.7 -6.8 -3.9 -1.0 1.9 4.8 7.7 Nominal lgCPUE BFT Figure 3. Observed log cpue frequency distribution of positive trips of ABFT by age class.

9 50

40 No. baitboats Getaria No. baitboats Hondarribia

20 No. baitboats No.

0 1975 1980 1985 1990 1995 2000 2005

Figure 4. Number of vessels of the Bay of Biscay baitboat fleet targeting ABFT that are based at the two ports.

Age1 40 Age2 Age3 Age4 Age5+ 20 Age frequency in the caatch frequency Age

0 June July August September October November

Figure 5. Mean proportion of the ABFT baitboat catch by age during the fishing season.

10 Age 1 Observed 12 Index Low conf. Inter. 8 Upper conf. Inter.

scaled index scaled 4

0 1975 1980 1985 1990 1995 2000 2005

6 Age 2

scaled index scaled 2

0 1975 1980 1985 1990 1995 2000 2005

Age 3 6

scaled index scaled 2

0 1975 1980 1985 1990 1995 2000 2005

12 Age 4

scaled index scaled 4

0 1975 1980 1985 1990 1995 2000 2005

20 Age 5 +

8 scaled index scaled

0 1975 1980 1985 1990 1995 2000 2005

Figure 6. Standarized scaled to the mean cpue of baitboat-caught ABFT for the delta-lognormal model. 11 Index CVs 0 0 0 1 1 1 2 2 2 3 3 3 Age Age 4 4 4 5 5 5 6 6 6 1974 1980 1986 1992 1998 2004 1974 1980 1986 1992 1998 2004 Year Year

Index CVs 0 0 0 1 1 1 2 2 2 3 3 3 Age Age 4 4 4 5 5 5 6 6 6 1974 1980 1986 1992 1998 2004 1974 1980 1986 1992 1998 2004 Year Year

Figure 7. Standarized indices and corresponding CVs for the delta-lognormal model. The indices and CVs have been scaled within an age to allow patterns in the series to be more easily seen, and the average magnitudes by age of the CVs are shown in the panel on the right. Indices less than average are shown in white and those greater than average are shaded. Top: time series from 1975 to 2004 (Rodríguez-Marín et al., 2007); bottom: time series from 1975 to 2007 (present study).

Age5+

-4

Age3 Age4

-4

Age1 Age2

2 Residuals positive observations model by age by model observations positive Residuals

-4

-3 0 3 qq plot normalized cumulative residuals positive observations by ag e

Figure 8. QQ-plots for the final model for positive catch rates of ABFT in the trip database.

12 Appendix

Nominal and estimated standardized catch rates (numbers of fish per trip) of ABFT by age in the Bay of Biscay baitboat fishery. Table also include the number of trips per year, coefficient of variation and 95% confidence intervals.

Age Year Nobs Nominal Standard CV Upp Low Age1 1975 278 19.80 19.50 30.17 35.19 10.81 1976 178 0.61 1.38 36.52 2.80 0.68 1977 258 6.01 1.50 34.49 2.94 0.77 1978 264 48.30 22.96 31.16 42.21 12.49 1979 172 0.03 0.04 125.25 0.28 0.01 1980 155 29.91 12.14 43.15 27.75 5.31 1981 143 44.13 50.51 36.74 102.91 24.79 1982 177 17.39 11.28 34.20 21.94 5.80 1983 224 226.67 93.97 31.54 173.97 50.76 1984 249 5.40 2.80 34.96 5.53 1.42 1985 337 9.70 9.60 33.13 18.30 5.03 1986 303 97.66 30.97 32.61 58.49 16.40 1987 264 22.15 16.38 31.46 30.28 8.86 1988 279 154.42 387.81 34.40 756.94 198.69 1989 347 50.02 194.33 30.01 349.63 108.01 1990 286 57.82 53.08 33.50 101.90 27.65 1991 299 45.99 55.19 32.68 104.36 29.19 1992 235 9.92 15.88 36.88 32.44 7.77 1993 373 16.74 7.61 36.53 15.45 3.75 1994 315 7.66 3.21 36.47 6.51 1.58 1995 436 77.45 85.55 29.45 152.30 48.05 1996 418 165.23 268.00 31.76 498.18 144.17 1997 307 83.76 120.19 32.11 224.90 64.23 1998 316 38.70 69.96 50.56 181.68 26.94 1999 222 3.58 8.94 70.30 31.76 2.52 2000 345 89.54 56.98 37.10 116.86 27.79 2001 236 1.67 4.90 38.80 10.37 2.32 2002 323 34.74 0.76 34.85 1.50 0.39 2003 159 1.05 1.58 81.05 6.55 0.38 2004 212 87.73 73.11 47.72 180.90 29.55 2005 405 132.29 114.28 31.57 211.68 61.70 2006 175 18.53 7.94 40.63 17.35 3.63 2007 242 0.00 Age2 1975 278 44.75 213.72 30.15 385.56 118.47 1976 178 33.40 146.32 36.06 294.44 72.71 1977 258 49.57 215.13 30.78 392.65 117.87 1978 264 37.37 75.11 31.07 137.83 40.93 1979 172 9.42 34.67 37.92 72.16 16.66 1980 155 22.78 69.41 41.80 154.89 31.11 1981 143 53.66 94.95 36.70 193.32 46.64 1982 177 29.70 128.09 33.88 247.64 66.25 1983 224 67.15 118.83 31.53 219.94 64.20 1984 249 129.21 485.62 34.82 955.31 246.86 1985 337 99.67 382.16 31.14 702.27 207.96 1986 303 38.72 87.36 32.35 164.20 46.48 1987 264 105.97 420.30 31.25 773.94 228.25 1988 279 38.55 51.80 32.27 97.21 27.60 13 1989 347 84.60 488.14 29.48 869.45 274.05 1990 286 32.41 108.78 31.78 202.28 58.50 1991 299 58.46 171.11 31.77 318.14 92.03 1992 235 68.18 254.59 32.93 483.65 134.01 1993 373 168.34 432.27 33.93 836.45 223.40 1994 315 31.96 34.84 29.69 62.31 19.48 1995 436 60.44 194.75 28.86 342.92 110.61 1996 418 71.09 167.51 31.04 307.21 91.33 1997 307 34.89 127.41 30.38 230.81 70.33 1998 316 48.07 63.56 31.55 117.68 34.33 1999 222 6.42 3.37 46.98 8.24 1.38 2000 345 32.30 44.40 33.29 84.91 23.21 2001 236 123.17 336.76 37.79 699.28 162.18 2002 323 62.48 359.87 33.42 689.90 187.71 2003 159 44.74 83.22 49.42 211.96 32.67 2004 212 80.35 87.65 33.25 167.50 45.87 2005 405 84.90 134.47 30.57 244.48 73.96 2006 175 71.34 104.88 38.23 219.52 50.11 2007 242 45.30 23.12 38.11 48.29 11.07 Age3 1975 278 10.08 48.74 30.50 88.49 26.85 1976 178 15.04 63.04 36.00 126.72 31.36 1977 258 24.73 100.63 30.77 183.66 55.14 1978 264 8.08 14.67 32.29 27.55 7.82 1979 172 20.73 71.91 38.09 150.14 34.44 1980 155 9.80 27.20 41.79 60.69 12.19 1981 143 5.42 5.47 36.71 11.14 2.69 1982 177 12.02 42.67 34.39 83.29 21.86 1983 224 9.19 14.60 34.20 28.39 7.51 1984 249 36.94 126.38 34.81 248.54 64.26 1985 337 31.44 110.43 30.92 202.11 60.34 1986 303 7.44 24.32 32.33 45.69 12.95 1987 264 3.83 10.80 36.73 22.00 5.30 1988 279 5.23 6.97 37.80 14.48 3.36 1989 347 8.61 8.26 31.76 15.36 4.44 1990 286 16.96 40.68 33.17 77.63 21.32 1991 299 11.88 23.88 32.48 44.99 12.68 1992 235 7.10 12.04 33.36 23.06 6.29 1993 373 61.85 185.98 33.83 359.24 96.28 1994 315 19.85 44.93 29.64 80.27 25.15 1995 436 23.34 63.03 29.17 111.62 35.59 1996 418 64.35 129.24 32.65 244.23 68.39 1997 307 62.31 196.79 29.79 352.59 109.83 1998 316 18.21 50.10 31.64 92.94 27.01 1999 222 4.52 13.21 38.14 27.60 6.32 2000 345 8.93 34.65 41.61 77.04 15.58 2001 236 28.61 64.42 40.44 140.30 29.58 2002 323 47.94 153.96 35.25 305.29 77.65 2003 159 11.09 12.73 53.58 34.77 4.66 2004 212 9.85 10.71 43.95 24.82 4.62 2005 405 30.95 64.34 34.77 126.45 32.73 2006 175 22.54 12.37 37.49 25.55 5.99 2007 242 18.67 36.62 36.65 74.49 18.00 Age4 1975 278 4.25 20.09 33.57 38.62 10.45 1976 178 3.43 10.69 36.87 21.82 5.23

14 1977 258 6.68 22.53 30.78 41.11 12.34 1978 264 9.25 23.86 37.10 48.93 11.64 1979 172 19.46 92.94 37.85 193.21 44.71 1980 155 4.24 16.92 43.32 38.79 7.38 1981 143 2.01 1.53 51.91 4.07 0.58 1982 177 4.27 11.82 35.40 23.50 5.95 1983 224 1.04 1.95 44.45 4.57 0.84 1984 249 6.52 20.20 34.86 39.75 10.26 1985 337 5.26 1.68 42.26 3.77 0.75 1986 303 5.00 9.77 35.14 19.33 4.94 1987 264 3.82 6.70 42.09 15.03 2.99 1988 279 1.76 1.54 42.39 3.47 0.68 1989 347 1.43 0.98 50.39 2.53 0.38 1990 286 6.21 6.79 35.20 13.45 3.43 1991 299 5.23 7.07 45.09 16.70 2.99 1992 235 1.61 3.20 61.31 9.92 1.04 1993 373 18.93 65.40 36.08 131.64 32.49 1994 315 7.60 11.84 29.87 21.24 6.60 1995 436 1.91 1.76 33.35 3.37 0.92 1996 418 30.79 62.50 37.23 128.49 30.40 1997 307 17.13 17.48 30.06 31.49 9.71 1998 316 38.86 63.89 33.81 123.38 33.09 1999 222 10.71 32.77 40.55 71.52 15.02 2000 345 4.99 23.05 41.45 51.11 10.40 2001 236 7.84 11.23 46.17 27.06 4.66 2002 323 3.59 3.88 46.09 9.33 1.61 2003 159 4.41 10.39 45.88 24.90 4.33 2004 212 8.84 4.66 39.63 10.00 2.17 2005 405 4.59 3.94 38.87 8.34 1.86 2006 175 2.77 2.78 41.53 6.17 1.25 2007 242 17.75 27.18 36.75 55.38 13.34 Age5+ 1975 278 1.89 0.53 38.48 1.12 0.25 1976 178 2.13 3.75 54.81 10.46 1.35 1977 258 0.67 1.57 43.28 3.59 0.68 1978 264 3.77 12.23 47.25 30.03 4.98 1979 172 6.69 26.14 37.81 54.30 12.58 1980 155 9.23 32.38 49.92 83.18 12.61 1981 143 1.04 0.76 76.07 2.94 0.20 1982 177 2.31 5.22 53.52 14.25 1.91 1983 224 0.14 0.25 102.42 1.35 0.05 1984 249 2.26 0.04 42.35 0.08 0.02 1985 337 1.75 0.44 70.88 1.58 0.12 1986 303 0.99 3.23 50.29 8.34 1.25 1987 264 3.48 4.62 49.16 11.71 1.82 1988 279 2.00 2.20 63.18 7.00 0.69 1989 347 0.50 0.58 63.01 1.85 0.18 1990 286 1.73 1.97 54.49 5.47 0.71 1991 299 2.40 0.84 49.62 2.16 0.33 1992 235 0.64 0.76 65.55 2.52 0.23 1993 373 3.70 6.91 45.44 16.44 2.91 1994 315 0.43 0.27 36.41 0.54 0.13 1995 436 0.29 0.25 68.62 0.88 0.07 1996 418 5.84 11.18 41.49 24.81 5.04 1997 307 2.53 4.32 38.89 9.15 2.04

15 1998 316 3.00 5.47 38.01 11.41 2.63 1999 222 21.54 78.48 41.85 175.29 35.14 2000 345 10.96 20.21 45.65 48.24 8.46 2001 236 4.87 4.02 57.48 11.69 1.38 2002 323 0.52 0.98 64.25 3.16 0.30 2003 159 6.70 7.07 59.88 21.40 2.34 2004 212 4.49 8.71 42.09 19.52 3.88 2005 405 2.78 5.12 47.59 12.64 2.07 2006 175 3.82 4.45 47.31 10.92 1.81 2007 242 6.73 12.72 39.24 27.11 5.97