Analysis of Atlantic bluefin tuna catches from the last Tonnara in the Mediterranean Sea: 1993–2010

Fisheries Research 127–128 (2012) 133–141

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Analysis of Atlantic bluefin tuna catches from the last Tonnara in the Mediterranean Sea: 1993–2010 Piero Addis ∗ , Marco Secci, Ivan Locci, Angelo Cau, Andrea Sabatini Department of Life Sciences and Environment, University of Cagliari, 1 via Tommaso Fiorelli, 09126 Cagliari, Italy

a r t i c l e

i n f o

Article history: Received 30 March 2012 Received in revised form 15 May 2012 Accepted 17 May 2012 Keywords: Bluefin tuna Trap fishery Time series Demographic structure Mediterranean Sea

a b s t r a c t In the last 20 years, several factors have heavily impacted the Atlantic bluefin tuna fishery. They include management policies, market changes, the ban of the drift-net fishery, expansion of the modern purse seine fleet, and implementation of a stock recovery plan. To enhance current knowledge about the population’s status, we conducted a long-term analysis (1993–2010) of scientific data and standardized catch-per-unit-of-effort (CPUE) from the traditional trap fishery of Sardinia (Western Mediterranean, Italy), which is the last active bluefin trap fishery in the Mediterranean. We detected a significant increase of the standardized CPUE and a significant decrease in mean weight over time. Cluster analysis conducted on 29,000 specimens revealed three different size groups that were distinct by time period: the 1993–1995 period was characterized by a significant presence of large bluefin; a decrease in mean weight occurred in the 1996–2006 period; and 2007–2010 was characterized by the prevalence of young adults in the history in the trap fishery. This trend, which needs to be confirmed over longer time and spatial scales, raises some ecological questions. In particular, is the occurrence of these young adults a consequence of changes in the migratory behavior of bluefin tuna in the Mediterranean, or does it reflect the actual demography of this population? The results of this study emphasize that data from traditional traps provide valuable long term scientific information about population parameters through time, and thus the use of traps as monitoring stations should continue in the future. © 2012 Elsevier B.V. All rights reserved.

1. Introduction The Atlantic bluefin tuna, Thunnus thynnus (Linnaeus 1758), is one of the world’s most important fishing resources. Since ancient times, T. thynnus has provided a significant food supply in many coastal communities, and it has been an integral part of the globalization of the fish market. This pelagic species, which is distributed throughout the Atlantic Ocean and the Mediterranean Sea, is currently considered to be overexploited, and the International Commission for the Conservation of the Atlantic Tunas (Council Regulation EC n. 1559/2007) has therefore included bluefin tuna in a multiannual recovery plan. To achieve the plan’s objectives, the Standing Committee of Research and Statistics (SCRS) of ICCAT has recommended the progressive cutback of total allowable catch from 28,500 MT in 2008 to 12,900 MT in 2012 (Council Regulation EC n. 44/2012) and an increase of the minimum size of capture from 10 kg to 30 kg for both the Eastern Atlantic and the Mediterranean Sea. In addition, the ICCAT has launched a scientific research programme (GBYP) in 2010 to obtain new biological and ecological

∗ Corresponding author. Tel.: +39 070 675 8082; fax: +39 070 675 8022. E-mail address: [email protected] (P. Addis). 0165-7836/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.fishres.2012.05.010

information and data sets on captures that can be used to upgrade models for stock assessment, which is the most sensitive process for the conservation of the resource. In coming years, it will be crucial to assess the effectiveness of these conservation measures through continuous monitoring of the resource. Carefully designed monitoring systems generally should include fishery-independent data from surveys conducted on fine spatial and/or temporal scales (Hilborn and Walters, 1992). However, the complexity of the bluefin tuna fisheries makes such an approach impracticable, and fishery-dependent data and commercial landings are the only sources of useful statistics. The Mediterranean Sea is home to multiple pelagic fisheries ranging from modern purse seine and long-line fleets, which operate on the high seas, to minor small-scale coastal fisheries that use hand lines, trolling, rod and reel, and traditional traps. Prior to the end of the 1960s trap fisheries have provided nearly all Mediterranean data on bluefin tuna, and information about the interactions of the stock with other gears was virtually absent (ICCAT, 1997). Although trapping typically is a low-yield activity compared with modern fishery techniques, traps make a significant scientific contribution because they provide stable and continuous data collection, and these data supply abundance estimates (Ravier and Fromentin, 2001). For example, fluctuations of the

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stock over time and their relationships with abiotic and biotic factors have been investigated using time-series analysis of trap data (Fromentin et al., 2000a; Lemos and Gomes, 2004; Ravier and Fromentin, 2004; Addis et al., 2008). In fisheries research, one of the most common indexes of stock abundance is the catch-per-unit-of-effort (CPUE), and the assumption is that a proportional change in CPUE will represent the same proportional change in stock size (FAO, 1999). However, fishery scientists have raised concerns about the use of CPUE for active capture methods because it poorly reflects the underlying dynamics of fish populations, as catches are directly affected by fishing effort. Traps are an exception to these uncertainties (Ravier and Fromentin, 2001) because they passively capture fish, they are placed at fixed sites, and very few modifications have been made to the gear configuration over time; these factors all result in a stable fishing effort (ICCAT, 2006). Thus, traps are an ideal sampling method for continuous monitoring of a target species because they provide unbiased estimates of fish population parameters. If this assumption is accepted for both Mediterranean and Atlantic traps, we can use the data generated from them to understand changes in the stock over time and to evaluate the relationship between the Mediterranean and Atlantic stock. Traps currently are used at 18 sites in the Eastern Atlantic and at 2 sites in the Mediterranean: Morocco (10), Spain (5), Portugal (3) and Italy (2). In the Mediterranean, traps are deployed in southwestern Sardinia, providing an annual yield of about 120 tons (±58) of bluefin tuna. Starting in 1992, we began a monitoring program with the objective of collecting scientific data on bluefin catch in Sardinia and to obtain information about the bio-ecology of the bluefin population during the reproductive migration. In this paper we provide comprehensive information with reference to the period 1993–2010 on trap fisheries in Sardinia. In detail, we (i) analyze the CPUE temporal trend for the Isola Piana and Portoscuso traps, (ii) test for location and time effects on catches using a general linear modeling (GLM) approach, (iii) detect temporal patterns in the age and size structure of bluefin tuna catches. Finally, we discuss relationships between the patterns of variability in Mediterranean and Atlantic trap catches (both old and new) and their likely sources in order to construct an overall picture of the species exploitation in the two basins.

Table 1 Technical features of Sardinian traps (tonnare).

Lat/Long Number of rooms Tail length (m) Depth at rooms (m) Cross orientation Mouth position Mesh size Bottom Distance between traps (nM)

Isola Piana

Portoscuso

39◦ 11 /08◦ 18 E 5 1050 42 NW-SE SE 40 cm sand Isola Piana ↔ 3.0 ↔ Portoscuso

39◦ 14 /08◦ 22 E 5 2500 35 NW-SE SE 40 cm sand

42 m) and then cross from east to west into the other four chambers through the doors (i.e., a system of man-operated moving nets (Fig. 1, Table 1)). 2.2. Fishing data Data used for the analysis described herein, consist of the number of bluefin tuna captured by trap (fish captured in trap harvests “mattanzas” plus those entangled) during the period 1993–2010 (n = 29,885 individuals); the number of workable days of the gear within the time period between the first day in which a bluefin is captured until the last capturing day (i.e., the fishing effort for the traps); and total weight (in kg) of single specimens. Age estimates were made using the Von Bertalanffy growth parameters: L∞ = 318.9; K = 0.093; t0 = −0.97 (Cort, 1991). 2.3. Data analysis To investigate the temporal variability of catches and to generate standardized information about CPUE that took into account likely sources of variation, we used a general linear modeling (GLM) approach (Gavaris, 1980) to conduct analysis of variance. We assumed a negative binomial error with log-link and examined the logged catch rates expressed as catch in number of individuals per number of fishing days per year. We evaluated the effects of year, month, and trap. The general linear model used was: ln(CPUE) =  + Yi + Mj + Tk + Mi ∗ Tk + eijk

2. Material and methods 2.1. Sampling area and trap description The study area lies in the southwestern part of Sardinia (Italy). Since the sixteenth century, bluefin tuna traps have been used in two locales, Portoscuso (PS) (39◦ 14 /08◦ 22 E) and Isola Piana (IP) (39◦ 11 /08◦ 18 E). Nowadays they are managed by the private company Ligure Sarda. These traps are classified as “tonnara di corsa” (tuna on the path) because captured bluefin tuna during the pre-spawning migration and trapped specimens are characterized by having ripening gonads. The gear used consists of nylon nets arranged in a tail (∼1100 m for both traps) and five chambers: the Grande, the Bordonaro, the Bastardo, the Camera di ponente, and the Camera della morte (death chamber). Only the death chamber has a net mesh “floor” that is used to draw up bluefin tuna during the “mattanza”. The series of chambers together form what is known as the castle, whereas the tail is perpendicular to the trap and reaches almost to the coastline. In late April-early May, bluefin tuna migrate along the western coast of Sardinia in from north to south, as demonstrated by the historical position of traps and trap mouths. Tuna enter the trap swimming naturally, go up the tail, and cross the trap mouth. Once inside, they are enclosed initially in the largest chamber (Grande, 120 m × 45 m; bottom depth

where  is the intercept, Yi is the effect of the year, Mj is the effect of month, Tk is the effect of trap, Mj *Tk is the interaction between month and trap, and eijk is the error term. Annual abundance indices were obtained from least squares mean estimates adjusted for the GLM statistically significant terms. Weight data were explored using frequency distributions and multivariate methods. Estimated age-based distributions were obtained from ordinary length frequency distributions using the software LFDA 5.0 (Kirkwood et al., 2001), which allows conversion to age frequencies using the estimated growth curves. Temporal patterns in the data were investigated using nonparametric multivariate analyses (Clarke, 1993; Field et al., 1982) in the PRIMER Package (Version 6.0, Plymouth Marine Laboratory, UK). Raw data for these analyses consisted of a matrix of mean weights for each year, grouped by trap. These data were first normalized to make them comparable, and then a distance matrix (Euclidean distance) for years was calculated. Clusters were produced using a group-average hierarchical sorting strategy. Relationships between years, based on bluefin tuna mean weights, were examined using dendrogram plots. The differences-by-year clusters were identified using SIMPROF (␲ value) (Clarke et al., 2008). Time series of mean weights were also inspected by means of change-point analysis using the STARS method (Sequential t-test Analysis of Regime Shifts), an algorithm based on sequential t-test

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Fig. 1. South-western region of Sardinia where the traps are deployed.

analysis for detecting and testing regime-shifts (Rodionov, 2004). For this analysis a significance level of 0.05 and a cut-off length of 5 years have been chosen. Finally we tested the temporal trends using the Mann–Kendall test (MK test ␶), and we evaluated differences between weight frequency distributions using the Kruskal–Wallis test (KW test) (Helsel and Hirsch, 2002).

3. Results The analysis of variance (Table 2) identified substantial differences in bluefin tuna catches that were related to years and months (P < 0.01). In contrast, neither the location of the trap nor its interaction with month was a significant factor (P > 0.1). The model that incorporates all independent variables and interactions explains 87% (R2 ) of the annual variability of bluefin tuna catches. The analysis of standardized CPUE revealed an oscillating signal in the data, with a clear increasing trend in catches that was statistically significant ( = 0.451; ˛ = 0.05). Specifically, we observed a slight increase continuously throughout the entire data set. From 1997 until 2001, we recorded an appreciable increase in catch. As a result, the mean CPUE was at a higher level than in the 1993–1997 period (2.04 vs. 1.25, respectively). A sharp decrease occurred in the period 2001–2005 (reaching a minimum value of 1.0 in 2005) followed by an increase until 2010 (Fig. 2). The analysis of age distribution by year (Fig. 3) showed that during the 1993–1995 period, mainly the older age classes were dominant in the trap catches. From 1996 to 2006, younger individuals (age classes 4, 5, and 6) began to be captured more consistently. In 2007, a peak of young adults (age class 4) was caught by the traps. The effect of this important recruitment can be followed in the subsequent years (i.e., age classes 5, 6, 7 in 2008, 2009, and 2010, respectively): in these last three years of the study, we detected a

progressive decrease of age class 4 and an increase of the larger classes. The exploration of catch-at-weight enabled us to analyze these results in greater depth. The analyses show considerable changes in size structure, mean weights, and number of catches during the periods of study (Fig. 4). In particular, the general trends of the data, emphasized by the statistically significant decreasing tendency of mean weight ( = −0.4510; ˛ = 0.05), led us to identify three different patterns within the investigated time period (1993–2010). In the first period (1993–1995), catches of bluefin tuna were in general fewer in numbers and characterized by relatively high mean weights and wide size distributions. Between 1995 and 1996, we observed a decline of mean weights, which remained almost constant for the period 1996–2006 (Fig. 4). In the same period, there was a significant reduction of large fishes (>300 kg), followed by an increase in the number of smaller individuals. This phenomenon resulted in a considerable contraction of the demographic structure of the catches. A further contraction of the population together with a slight increasing trend in the mean weight occurred from 2007 to 2010. Classification of the tuna’s mean weight by cluster analysis generated a clear separation of the catch data into three year groups (1.5 distance), which corresponded to those previously described (Fig. 5). The first group included the 1993–1995 time period at a distance of approximately 2.78 ( = 0.17; Sig(%) = 0.1); the second (1996–2006) and the third group (2007–2010) were separated by a distance of 1.93 ( = 0.09; Sig(%) = 0.9). We finally highlight another possible subdivision, which splits the second group into two groups at a distance of 0.76, but is not statistically significant. The STARS method allowed us to identify three different regimeshifts (Fig. 6). The highest regime shift index was achieved in 1996 (RSI = −0.420), followed by a second one of a minor entity in 2007 (RSI = −0.329), indicating two significant regime shifts within the

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Table 2 Analysis of variance table for generalized linear model fitted to bluefin tuna (T. thynnus) CPUE data (no. of individuals/no. of fishing days) from 1993 to 2010 in SW Sardinia. Source of variation

Sum of squares

Df

Mean square

F-ratio

P-Value

Year Month Trap Month*Trap Residual Total (corrected)

19.9557 42.3761 0.00261 1.73563 13.4218 100.429

17 2 1 2 34 56

1.17386 21.1881 0.00261 0.867817 0.39476

2.97 53.67 0.01 2.20

0.0033 <0.0001 0.9357 0.1265

Fig. 2. GLM standardized CPUE for the Sardinian bluefin tuna (T. thynnus) trap fishery. Dashed lines indicate 95% confidence limits.

time series. In particular after an average run of about 107 kg within the period 1993–1995, a strong downward shift have interested the next 11 year period (1996–2006), with an average run of 67.2 kg. Afterwards (2007–2010), a new low-level regime was established with an average run of 40.5 kg. Comparison of weight distributions within the three identified periods revealed a clear difference (P < 0.01, KW test) among them in terms of bluefin tuna catches (Fig. 7). We detected a progressive contraction of the size distribution from the first to the most recent periods, with a significant increase in numbers of small individuals and a decrease of larger ones. In particular, 1597 fishes with a minimum weight of 17 kg, a maximum weight of 474 kg (mean weight 110 kg) and a mode of 100 kg characterized the catches during the first period. Catches increased in the second and third periods (13,341 and 14,606 fishes, respectively). Simultaneously, the average weight and the mode progressively declined (67 kg and 50 kg vs. 41 kg 37 kg for the second and third period, respectively),

which emphasizes the small numbers of large individuals present during the 4 year period of 2007–2010 (Fig. 7A). The same pattern appeared when data from the two traps were analyzed separately (Fig. 7B and C). To investigate on the role of month of the year on bluefin tuna catches, we conducted a further analysis of the weight frequency distribution by month within periods. The results confirm the prior evaluation of size changes over the course of periods (Fig. 8) in that they highlight a contraction of the size range. This is evident in the 1993–1995 time period (excluding April, because no data were available), when consistent numbers of individuals >200 kg were present and accounted for >15% of the overall captures in May and June (Fig. 8A). In the 1996–2006 period, the size structure of the population contracted significantly. This is particularly obvious in May, when bluefin >200 kg were uncommon (Fig. 8B). During the last period (2007–2010) we detected a correspondence between the size structure within months, as the same mode was present

Fig. 3. Estimated age distribution for the Sardinian bluefin tuna (T. thynnus) trap fishery by year, weighted for the number captured in annual total catches.

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Fig. 4. Bubble plot of catch-at-weight composition of bluefin tuna (T. thynnus) captured in the Sardinian trap fishery by year.

and a further reduction of large fish (only 20 per year) occurred (Fig. 8C). 4. Discussion Based on a substantial database collected as part of a scientific monitoring program, we analyzed CPUE and the demographic structure of bluefin tuna captured in the last two traps operating in the Mediterranean. The analyses identified a positive trend in CPUE with a secondary oscillating signal and a substantial decrease in mean weights over time. These results indicate that a significant

increase in the number of small bluefin tuna occurred, mostly during the 2007–2010 period. On a broader scale, the trend we observed was common to the Atlantic traps as well (Abid and Idrissi, 2010; Ortiz de Urbina et al., 2011a), although there is a different demographic structure in the two fisheries. Our observation highlighted that beginning in 1997 the CPUE increased and reached a peak in 2001. This pattern completely matches those identified for the Moroccan traps in 1995–2001 (Abid and Idrissi, 2010) and matches to a lesser degree the trends from the Spanish traps, which exhibited a positive trend followed by a decrease beginning in 1999 (Ortiz de Urbina et al., 2011a). These comparisons provide further

Fig. 5. Cluster analysis for mean weights of bluefin tuna (T. thynnus) captured in Sardinian trap fishery by year. P1, P2 and P3 indicate the three different periods identified: 1994–1993, 1997–2006, 2007–2010 respectively.

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Fig. 6. Shifts in the mean for weights per year, 1993–2010 (probability = 0.05, cutoff length = 5, Huber parameter = 1).

evidence for relationships between the Atlantic and Mediterranean areas as already observed by Fromentin and Powers (2005). We do not exclude the possibility that the observed increase in CPUE could be related to the reduction of fishing pressure from the drift net fleets. In 1997, the European Community established technical measures to limit drift nets in the Mediterranean (Council Regulation EC 894/97). Since drift nets were frequently deployed close to the trap area, we hypothesize that they may have caused a physical interaction or a “barrier effect” to the migration pathways of bluefin tuna towards the traps, thereby affecting trap catches. CPUE of Moroccan and Spanish traps dropped rapidly from 2001 until 2004 (the decline began in 1999 for the latter) (Ortiz de Urbina et al., 2011a). This period corresponds to the greatest expansion of purse seine fisheries (614 vessels in 2008), which also harvest large bluefin tuna during the spawning period (Fromentin and Powers, 2005; WWF, 2008). During the last period (2007–2010), Sardinian traps shown a substantial increase in CPUE, which corresponded to the occurrence of a large fraction of specimens belonging to age class 4. The same capture pattern characterized the Atlantic traps,

except that catch by age increased significantly for adult bluefin tuna belonging age classes 9 and 10, whereas age class 4 were negligible in the Atlantic (Ortiz de Urbina et al., 2011b). The factor location did not revealed a variability in catches in Sardinian traps, even though the traps are separated by 3 nautical miles. Literature clearly documented interactions among neighboring gears for Moroccan and Spanish traps (Abid and Idrissi, 2010; Ortiz de Urbina et al., 2011a). Thus, presumably adjacent gears deployed along a straight coastline had a “filter” effect that affected catches. The ecological interpretation of this pattern is that a single pathway of migration (likely a northward flow) of bluefin tuna encounters the Moroccan traps. According to captures by Japanese long-line in the Strait of Gibraltar, a similar interaction occurs in Spanish traps, which likely intercept the migration from north to south (Suzuki, pers. com.; Cort, pers. com.). Our finding suggest that a migrating school of bluefin tuna (confirmed by the homogeneity of size structure between Sardinian traps) migrates following diverse migration pathways towards the two traps. The pathway of interest relative to the Isola Piana trap

Fig. 7. Bluefin tuna (T. thynnus) size composition comparison between periods; for the whole area (A) and separately for Isola Piana (B) and Portoscuso traps (C).

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Fig. 8. Bluefin tuna (T. thynnus) size composition comparison between months within the different time periods: 1993–1995 (A); 1996–2006 (B); 2007–2010 (C).

would flow west to east, thus it would come from the west of the trap area. The frequent records of entangled bluefin tuna in the west side of the trap tail supported this scenario. The pathway towards the Portoscuso trap should flow north to south along the western coast of Sardinia, as demonstrated by the map of the historical locations of 15 traps used in the twentieth century (Angotzi, 1901). The GLM highlight that time-related factors affected CPUE. In particular, during 1993–1995 and to lesser extent in 1996–2006 we detected monthly differences, when large individuals occurred mainly in May rather than in June. In the last period, 2007–2010, we detect no differences in monthly size distribution because the large individuals disappeared from the catches. Finally, yearly variability occurred due to the increasing fraction of juveniles present in the catches over the course of the diverse phases analyzed. Because we identified no differences between the catches of the two Sardinian traps, we combined the data from both traps and analyzed catch-at-size data using multivariate analysis. The results revealed three separate periods characterized by

diverse mean weights. The largest bluefin tuna were caught during the first period (1993–1995), with individuals up to 470 kg captured frequently. Prior to the period 1993–2010, catches of large bluefin tuna were documented, specifically in the trap at Isola Piana. Catches of such large individuals were common in Sardinian traps, since they included one of the world’s largest individual ever caught (685 kg, Stintino trap, north Sardinia) (Sarà, 1969). In the literature, comparative catch and demographic data are available for the no-longer-active Tunisian and Libyan traps in the Mediterranean (Hattour, 2004; Tawil et al., 2007). Data from those traps perfectly match data from the Sardinian traps for the same time period, that illustrates the homogeneity in the structure of the migrating stock of bluefin tuna in the western Mediterranean. In the last period, 2007–2010, 80% of captures consisted of bluefin tuna <6 years old. Beginning in 2007, age class 4 represented 74% of the total catch, whereas in the period 1993–1995, this proportion never exceeded 5%. In 2008 and 2009, age class 4 represented 40% and 25% of the total

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catch, respectively. This finding illustrates that a large recruitment occurred during 2003 but also in 2004 and 2005. According to ICCAT (2009), the 2003 year class is estimated to be the largest since 1974, but there are no data available for the 2004 and 2005 year classes identified herein. In the Atlantic traps, age class 4 or smaller never was detected during this same span of time and the target age of catches corresponds to 8 years and older specimens (Fromentin et al., 2007; Ortiz de Urbina et al., 2011b; Restrepo et al., 2007). The analyses also showed that major breakpoints in the decline of mean weights in Sardinian traps corresponded to the two major increases in CPUE in the Atlantic traps. This evidence may indicate a relationship between the two areas, but this interpretation is speculative at present. However, we view them as a starting point for a comparative analysis of the likely relationships between Atlantic and Mediterranean traps. The outcomes of this findings have led to further ecological questions, such as (i) Does demography of catches strictly represent the structure of the whole stock in each area? (ii) Are progressive changes in the migrating behavior occurring within the Mediterranean stock and/or between the Atlantic and Mediterranean, which could explain the occurrence of juveniles in the recent catches of Sardinian traps? We know that the life strategy of bluefin tuna is the so called “intermediate strategy,” as they tend to fluctuate from an opportunistic strategy to a periodic strategy (King and McFarlane, 2003). As opportunists, bluefin tuna respond with a fast and high amplitude change in biomass to the high variability of the habitat, including fishing pressure (Anderson et al., 2008). This strategy probably explains the large number of young adults caught in 2007, 2008, and 2009. As periodic strategists, bluefin tuna exhibit great longevity, which guarantees a relatively long reproductive cycle. Their highly migratory behavior guarantees that they move from areas of unfavorable conditions to areas of better conditions, thus minimizing the risk of loss in the stock (Polovina, 1996; McFarlane et al., 2000). The latter ecological characteristic is sometimes neglected when evaluating the population dynamics of bluefin tuna because it is easier to interpret responses using a speciesspecific approach rather than to enlarge the analysis to include an ecosystem approach. For example, the commercial disappearance of bluefin tuna from historical fisheries areas in the North Sea (Fromentin, 2009; MacKenzie et al., 2009) could be ascribed mostly to the loss of “habitat fitness” for bluefin tuna rather than to exclusively fishing-related causes. It is noteworthy that in the North Sea, many fishing areas for bluefin tuna corresponded to the oil and gas harvesting and drilling exploration fields, which in the late 1950s were extensively developed by Great Britain, Norway, and Denmark; in 2007 about 980 platforms were present in this area (Anonymous, 2012; MacKenzie and Myers, 2007). Bluefin tuna are especially sensitive to acoustic pollution (Sarà et al., 2007), and turbidity (Addis et al., 2008), thus the disappearance of this species from the North Sea could also be linked to environmental modification in that area. Fishery scientists also have the example of disappearance of bluefin tuna in the Black Sea caused by the decline in environmental quality between the 1970s and the 1980s (BSC, 2008). This process has resulted in the loss of a favorable reproductive habitat for bluefin tuna. We believe that fishery scientists should take into consideration whether such a process is occurring between Mediterranean Sea and regions of the Eastern Atlantic. Due to geographical segregation, many fish species form subpopulations with limited connectivity between them or with very distinct patterns of migration (Gaertner et al., 2008; Garcia de Leaniz et al., 2007; Traina et al., 2011; Turan, 2004). Bluefin tuna show a well-defined adaptation to specific environmental conditions corresponding to two major reproductive areas, the Gulf of Mexico and the Mediterranean Sea, with a bidirectional connection between them that affects both adults and adolescents (Rooker et al., 2008). At present, some researchers view bluefin tuna as

belonging to certain bluefin ecotypes and therefore forming patchy populations between the eastern Atlantic and the Mediterranean (Fromentin and Powers, 2005) and within the Mediterranean between the eastern and western basins (Corriero et al., 2005; Heinisch et al., 2008; Karakulak et al., 2004; Medina et al., 2002; ˜ et al., 2010). Vinas Despite the advent of biochemical and molecular genetics techniques that have greatly improved our ability to delineate stocks, the spatial structure of the bluefin tuna population remains unclear ˜ (Vinas et al., 2010). Scientific data collected using conventional tagging (Cort, 1991) and electronic tagging (Block et al., 2005; De Metrio et al., 2005; Medina et al., 2011) have suggested that during the reproductive season, there exists a Mediterranean “resident stock” that is supplied by Atlantic “immigrants” (and vice versa), but the degree of separation between them is difficult to evaluate and will require more study. In the natural environment, the establishment of physical or ecological barriers or prey-predator stress (such as fishing pressure) on the ecological niches of a population, might be an adaptive process that could result in discrete population demography (Hanski and Gilpin, 1997; Robinson and Parsons, 2002). If such an ecotyping process is affecting bluefin tuna, it will take a long time for this process to occur; such a long time scale poses difficulties in designing studies and uncertainty in interpreting data from current scientific investigations. For these reasons it is of the highest priority to continue the scientific monitoring of bluefin tuna by means of fixed and reliable sampling methods like the ancient tuna traps. We emphasize that traps provide valuable scientific information about the status of the Mediterranean bluefin tuna population. Uncertainties about the utilization of this kind of fishery in the future remain high because of catch allocation and seasonal closures. Unfortunately, the closure of the Tunisian and Libyan traps in recent years resulted in a significant lost of comparative data and created a gap in our data base about the bluefin tuna stock in the Mediterranean. Specific scientific programs would be considered in the future for the reopening of such traps, at least as scientific observatory. Such issue has been also highlighted at a recent ICCAT symposium on the trap fishery (Tangiers, Morocco-May 23–25, 2011). Moreover, the development of a scientific network to study and compare data from the last Atlantic and Mediterranean traps can provide long term synchronized data useful for understanding the relationships between the two Mediterranean basins evaluate the role of the Strait of Gibraltar, given that it represents the zone with a significant ecological discontinuity.

Acknowledgments We would like to thank to the Greco family and the Ligure Sarda Company (Carloforte), to all ‘tonnarotti’ fisherman of the Isola Piana and Portoscuso traps, who provided logistical support in the period 1991–2011. We thank John Mark Dean from the University of South Carolina for constructive discussions and field assistance; Pepe Cort (IEO) and Ziro Suzuki (JTF) for helpful suggestions; ICCAT secretariat and GBYP coordinator Antonio Di Natale, for having raised attention to the issue of the trap fishery. We appreciate the constructive criticism of two anonymous referees, that substantially improved the paper. This work was supported by grants from the Italian Ministry of Agriculture and Forestry Policies (4th and 5th Fishing and Aquaculture Plan, project no. 4A30) and Academy Funds from the University of Cagliari.

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