ICES CM 2001/J:01

Theme Session J

Morphometric and meristic variation in European hake, Merluccius merluccius, from the Northeast Atlantic and Mediterranean Sea

By

P. Abaunza1, S. Mattiucci2, G. Nascetti3, A. Magoulas4, R. Cimmaruta3, L. Bullini5

1Abaunza, Pablo. Instituto Español de Oceanografía. Apdo. 240, 39080, Santander, Spain. Tel: +34 942291060 Fax: +34 942 275072; e-mail: pablo.abaunza@st.ieo.es 2Mattiucci, Simonetta. Universitá di Roma “La Sapienza”. Istituto di Parassitología. Piazzale Aldo Moro No. 5, 00185 Rome, Italy. Tel: +39 0644230936; Fax: +39 0644230311 e-mail: smattiucci@axrma.uniroma1.it 3Nascetti, Giuseppe. Department of Enviromental Sciences. Tuscia University. Via S. Camillo de Lellis, I-01100, Viterbo, Italy. Tel: +39 07 61357123. Fax: +39 07 61357179. E-mail: nascetti@unitus.it 3Cimmaruta, Roberta. Department of Enviromental Sciences of Tuscia University. Via S. Camillo de Lellis, I-01100, Viterbo, Italy. Tel: +39 07 61357123. Fax: +39 07 61357179. E-mail: nascetti@unitus.it 4Magoulas Andonis., IMBC, Genetics department. Main Port, GR 710 013, Iraklio, Greece. PO box: 2214. Tel: +30 81393400 FAX: +30 81241882, e-mail: magoulas@imbc.gr 5Bullini, Luciano. Dipartimento di Genetica e Biologia Moleculare. University of Rome “La Sapienza”. Via Lancisi 29, 00161 Rome, Italy. Tel: +39 06 44230926, Fax: +39 06 44230311.

ABSTRACT:

Morphometric and meristic variation was used to investigate the European hake population structure. Samples were collected from the following selected areas: Sothwest of Ireland (Great Sole Bank), Bay of Biscay, Galician waters (Northwest of Spain), Atlantic coast of Morocco, and waters of the Balearic Islands. A total of 15 different measurements were taken from each of 289 fish collected. Three meristic characters were also counted in the specimens from Bay of Biscay, Galicia and Morocco. The morphometric measurements were standardized to a grand mean standard length using an allometric formula. Meristic and morphometric characters were analysed by univariate and multivariate statistics. Morphometric discriminant functions were able to correctly classify the 69% of the samples. The areas: Bay of Biscay, Balearic Islands and Great Sole had the higher levels of discrimination with a values greater than 73%. These results cast doubt on the current stock units defined in the Northeast Atlantic, suggesting a more complex stock structure in the so called “Northern stock”. ANOVA results on meristic characters showed significative

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differences, P< 0.05, between the samples from Morocco and Galicia. The results are discussed in relation with the diversity of the environmental conditions and compared with those provided with other techniques, mainly genetics, applied in European hake for stock identification purposes and obtained from the bibliography. INTRODUCTION: Hake, Merluccius merluccius (Linnaeus, 1758) is present throughout the northeastern coast of the Atlantic ocean, from Norway to the Atlantic coast of Morocco, and in the Mediterranean and Black seas (Svetovidov, 1986). It is a species with a high commercial value, for which reason it is actively fished throughout its area of distribution, with a mean catch between 1987 and 1996 of 112,000 t annually (FAO, 1998). As it is subject to strong exploitation (Alheit & Pitcher, 1995), fishing activity must be regulated in order to avoid over-exploitation, or even the disappearance, of the resource. Its population dynamic has to be taken into account in fisheries management, to which end the units or stocks to be submitted to assessment must be defined (Gulland, 1971). Each stock, in principle, should be homogeneous in production characteristics (growth, reproduction and mortality) such that the models used in population dynamics can be applied to them (Tyler & Gallucci, 1980). In order for identification to be compatible with the expected aims of management as much information as possible on the exploited resource must be collected (Begg & Waldman, 1999). In the identification of stocks several methodologies can be applied, such as those found in Ihssen et al. (1981), ICES (1996), Pawson & Jennings (1996). Among these methodologies is the analysis of morphometric and meristic data, which have been widely used in the identification of fish stocks (Meng & Stocker, 1984; Junquera & Perez-Gándaras, 1993; Elliot et al., 1995; Huribut & Clay, 1998; Murta, 2000; Saborido-Rey & Nedreaas, 2000) to determine taxonomic groups (Misra & Ni, 1983; Marcus et al., 1996; Gallo da Silva et al., 1998) and even to distinguish cohorts of a single species (Austin et al., 1999). Hake stocks are currently defined more by matters of administrative convenience than by biological certainties, a concern that has already been addressed in the ICES area (ICES, 2000), in the Mediterranean (Caddy, 1998) and in international projects (Bullini et al., 1998). In the Northeast Atlantic two stocks are considered for the purposes of assessment and management: The Northern Stock, covering the area from Cap Breton canyon in the south of the Bay of Biscay to the northern limit of its distribution, and the the Southern Stock, from the Atlantic continental shelf of the Iberian Peninsula (Casey & Pereiro, 1995). In the Mediterranean sea in general and for stocks of demersal species, the sub-areas defined by the GFCM (General Fisheries Council for the Mediterranean) seem to represent, a priori, reasonable limits for the stocks (Caddy, 1998). There are hardly any papers related to the application of morphometric methods to the European hake, while those related to meristic characteristics are more common. Inada (1981) revised the family merluccidae regarding morphometric and meristic features to distinguish among species rather than sub-populations. In his Paper he distinguishes Mediterranean hake from Atlantic hake based on meristic criteria of vertebral counts, assigning them to the category of subspecies.

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More recently, Orsi Rellini et al.(1994) distinguished two different types in the Mediterranean Sea, also by meristic analysis of vertebral counts: the Eastern and Western races. Meristic analysis was also applied by Bouaziz et al (1998) to identify Algerian populations from Atlantic ones. Torres et al., (2000) distinguised between the Mediterranean and the Atlantic populations of European hake, by applying the analysis of sagittal otolith size and shape variability. This paper attempts to discern the population structure of European hake in a wide area of its distribution (Bay of Biscay, NW of Spain, Atlantic coast of Morocco and the Mediterranean sea) through use of morphometric and meristic analysis. To contrast the variability observed, multivariate techniques are applied (principal components analysis and discriminant analysis) to 15 morphometric variables and three meristic variables. The results are discussed in relation to work published on the genetic structure of European hake and regarding the possible influence of environmental conditions on the morphometric variability observed.

MATERIAL AND METHODS:

Hake samples were collected in the Southeast of Ireland (Great Sole Bank = GS), in the southeast of the Bay of Biscay (ICES Division VIIIb = BB), in the area of Larache-Rabat (Atlantic coast of Morocco = MO) and in waters surrounding the Balearic Islands in the Mediterranean sea (BI), (see Fig. 1). Sampling is included in the general plan of the GENHAKE project (Bullini et al., 1998). All specimens were obtained from the catches of commercial trawl fleets during spring-summer 1998 (BB, MO and BI), and spring-summer 1999 (GS). In total, 291 specimens were collected for morphometric and meristic analysis (Table I). Once captured, specimens were immediately frozen with the aim of preserving the best conditions for their later laboratory analysis. 15 morphometric variables and three meristic characters were studied in each specimen (Table II and Fig. 2). In the choice of morphometric and meristic characters the criterium and results obtained by Inada (1981) were taken into consideration. Morphometric measurements were taken using a digital caliper to the nearest 0.1 mm and the meristic ones by simple visual inspection, all on defrosted specimens which still retained some degree of stiffness. This stiffness did not affect the measurements considered in this paper, except for the characteristic “BD: maximum fish height” which, as it does not represent the natural position of the fish, was not introduced in the analysis. The cold state had to be maintained as far as possible since the same specimens were to be analysed later for genetic and parasitological studies within the project GENHAKE (Bullini et al., 1998). Due to the state of the guts after a long frozen period, in the end it was impossible to determine the sex of most of the specimens. Therefore, the statistical analyses were made without distinguishing the sexes. Total fish length was measured to the lower mm, using an ichthyometer.

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Figure 1.- Distribution of hake stocks in the Northeast Atlantic, with the indication of sampling sites (pink circles).

30º W 10º W 0º 10º E 30º E

60º N

40º N

30º N

40º E

4

Figure 2.- Morphometric and meristic measures taken in each hake specimen

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Table I. Number of specimens collected and analysed by area through morphometric and meristic analysis.

AREA N N (Morphometry) N (Meristics) GS 56 56 - BB 60 60 60 GA 55 55 55 MO 60 60 60 BI 60 60 -

TOTAL 291 291 175 Tabla II: Morphometric and meristic characters analysed. Number Name Description Morphometric variables

1 TL Total length 2 BD Body depth 3 HL Head length 4 SL Snout length 5 DO Diameter of orbit 6 UJL Upper jaw length 7 LJL Lower jaw length 8 LS1D Length from tip of snout to first dorsal fin origin 9 LS2D Length from tip of snout to second dorsal fin origin 10 LSPc Length from tip of snout to pectoral fin insertion 11 LSPv Length from tip of snout to pelvic fin insertion 12 LSA Length from tip of snout to anal fin origin 13 PvL Pelvic fin length 14 PcL Pectoral fin length 15 IOL Interorbital length Meristic variables

1M 1DFR First dorsal fin rays 2M 2DFR Second dorsal fin rays 3M AFR Anal fin rays

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Statistical analysis Statistical analysis was applied to the morphometric and meristic variables separately due to their different nature, the meristic variables being discrete, whereas the morphometric ones are continuous. Morphometric analysis: All the morphometric measurements were transformed to common logarithms to obtain a better approximation to multivariate normality (Klingenberg, 1996). Only those specimens in which the 14 variables of the corresponding morphometric characters were available were taken into consideration in order to avoid the entry “no observations” in the data matrix. Data exploration was conducted to look for outliers. The points with very high residuals in the regression of morphometric variable on standard length were eliminated, assuming that they were due to measurement errors. The length distributions of the five sampling areas are different and therefore it was necessary to correct the influence of length on the values of the different morphometric characters to be able, in this way, to make a comparative analysis between samples. The measurements of the morphometric characters were standardised to mean total length using the following formula ( Reist, 1985; Fraser et al., 1998):

( )toos LLbYY loglogloglog 101010 −−= Where: Ys = estandardized morphometric variable; Yo = The uncorrected variable value (observed measurement); Lt = mean total length considered all the samples; Lo = Total length of each fish and b is the allometric coefficient for the respective character. This parameter b was considered common for all the areas (Elliot et al., 1995), and it was estimated as the slope of the relationship between log Yo and log Lo. Correlation coefficients between each pair of characters were calculated to check if the data transformation was effective in reducing the influence of size in the measurements. The variable total length was only used for the standardisation of the rest of the variables. Principal component analysis on size corrected morphometric variables was applied to identify which variables explain the majority of the variance observed. The objective was to identify the structure of our data, by explaining the observed variability with fewer variables or principal components. This is a way of recognising redundant information and of knowing how the variables relate to one another (Hair et al., 1999). For the calculation of principal components the correlation matrix was used due to the fact that its elements are standardised coefficients (Alvarez, 1995). The measure of sampling adequacy (MSA) for each variable was obtained from the diagonal of antiimagen correlation matrix. The variables which presented values clearly below 0.5 in the MSA (unacceptable range, Hair et al., 1999) were omitted for later analysis. To set the number of principal components we considered valid those whose eigenvalue

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was great than one (Everit and Dunn, 1992). To interpret easily the structure of principal components we used the orthogonal rotation of the factors, applying the VARIMAX method (Hair et al., 1999; SPSS 1999, version. 9.0.1.). To test if there is any difference among the pre-defined groups we applied a multivariate discriminant analysis on the variables selected previously after principal component analysis. In discriminant analysis, each discriminant function contains the percentage of the total between-groups variability whereas in principal component analysis we analysed the total diversity (Hair et al., 1999). The method applied was stepwise discriminant analysis in which the variables are entered one at a time according to an F-to enter criterion (P <= 0.05) (Junquera & Perez-Gandaras, 1993). Meristic analysis: It was quite difficult to have information from the total number of specimens. Some of them were damaged and the rays were loosed. For example, in area“GS”, it was no possible to count rays almost in any specimen. A one-way analysis of variance was applied to the original meristic counts to test for group differences. To test the equality of variances the Leven’s test was performed. All the statistical analyses were conducted using the software SPSS (1999, ver. 9.0.1.) RESULTS : Morphometric characters: The removal of size effect is impressive after applying the standardising method. Only the variables Upper Jaw length (UJL) and Lower Jaw Length (LJL) continues with a great correlation between them, thus representing redundant information (See table III). The principal component analysis can be applied to the morphometric data available because the measure of the sampling size adequacy (Kaiser- Meyer- Olkin measure) is 0.672, being values lower than 0.5 unacceptable (Hair et al., 1999). Also, because the Barlett’s test of esphericity (a test for the presence of correlations among variables) gives P < 0.001. From the anti-image correlation matrix the sampling size adequacy of each variable can be obtained. The matrix shows that the value of the variable DO is 0.369 which is unacceptable and then we have taken out this variable for further analysis.

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Table III.- Correlation coefficients between characters, before and after the removal of the size effect, are respectively shown below and above the diagonal.Values higher or equal to 0.95 are in bold.

Length HL SL DO UJL. LJL LS1D LS2D. LSPc. LSPv LSA PvL PcL IOLLength 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

HL 0.99 0.55 -0.18 0.40 0.37 0.41 0.25 0.34 0.23 0.47 0.11 0.17 0.32SL 0.98 0.98 -0.33 0.37 0.33 0.43 0.24 0.33 0.24 0.48 -0.08 -0.04 0.42DO 0.92 0.90 0.88 0.15 0.19 0.03 -0.06 0.14 -0.04 -0.14 -0.02 -0.03 -0.22UJL. 0.98 0.98 0.98 0.92 0.94 0.36 0.16 0.30 0.11 0.30 -0.07 -0.02 0.28LJL 0.98 0.98 0.97 0.92 1.00 0.35 0.16 0.33 0.09 0.28 -0.07 -0.05 0.25

LS1D 0.99 0.99 0.98 0.92 0.98 0.98 0.47 0.50 0.34 0.68 -0.03 0.01 0.31LS2D 0.98 0.98 0.97 0.91 0.97 0.97 0.99 0.28 0.16 0.42 0.07 0.09 0.12LSPc 0.99 0.98 0.97 0.92 0.98 0.98 0.99 0.98 0.61 0.49 -0.10 -0.13 0.22LSPv 0.95 0.95 0.94 0.87 0.94 0.94 0.96 0.94 0.97 0.43 -0.07 -0.03 0.14LSA 0.99 0.99 0.98 0.91 0.98 0.98 0.99 0.99 0.99 0.96 -0.02 -0.02 0.30PvL 0.97 0.97 0.95 0.90 0.96 0.96 0.96 0.96 0.96 0.92 0.97 0.60 -0.08PcL 0.97 0.97 0.95 0.90 0.96 0.95 0.96 0.96 0.95 0.92 0.96 0.98 -0.02IOL 0.98 0.98 0.97 0.88 0.97 0.97 0.98 0.97 0.97 0.94 0.98 0.95 0.95

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Figure 3.- Scree diagram

Scree diagram

Number of component

121110987654321

Eige

nval

ue

5

4

3

2

1

0

The figure 3 shows the scree diagram for the selection of the four principal components. The explained variance by each principal component is shown in table IV. The four components selected represent the 70% of the variability observed which lies among the usual values in biological multivariate analysis. After the orthogonal rotation the final scores for the coefficients for the calculus of the punctuations in the components are shown in table V.

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Table IV.- Results of the principal component analysis, showing the variance explained by the four components selected.

Total Variance explained

3.826 31.885 31.885 3.826 31.885 31.885 3.060 25.500 25.5001.706 14.215 46.099 1.706 14.215 46.099 2.273 18.945 44.4451.514 12.614 58.713 1.514 12.614 58.713 1.700 14.163 58.6081.373 11.442 70.155 1.373 11.442 70.155 1.386 11.547 70.155.911 7.589 77.744.728 6.063 83.807.498 4.151 87.958.418 3.485 91.443.361 3.008 94.452.342 2.852 97.304.262 2.187 99.491

6.112E-02 .509 100.000

Component123456789101112

Total% of thevariance

%accumulat

ed Total% of thevariance

%accumulat

ed Total% of thevariance

%accumulat

ed

Initial eigenvaluesums of the saturations to the squar

of the extractionums of the saturations to the squar

of the rotation

Extraction method: Principal Component Analysis.

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Table V

.108 .151 .163 -.006

.064 .063 -.022 .591-.119 .475 -.021 -.006-.122 .473 -.033 -.014.247 .006 .018 .037.209 -.065 .114 .078.265 -.055 -.110 -.063.305 -.197 -.083 -.049.281 -.045 .011 .010.002 -.017 .510 -.028

-.004 .009 .518 -.007.053 .082 .012 -.599

HLSLUJLLJLLS1DLS2DLSPCLSPVLSAPVLPCLIOL

1 2 3 4Component

Component 1 is mainly represented by the variables LSPc, LSPv, LSA, LS1D and LS2D all of them variables of length from the snout to the insertion of fins. Component 2: is mainly represented by the variables: UJL and LJL (the jaw lengths). Component 3: is mainly represented by the variables: PvL and PcL (pectoral and pelvic fin lengths) Component 4: is mainly represented by the contrast of the variables: SL (positive) and IOL (negative). Because these components are extracted from size-adjusted data, the Principal Components including the first component , describe shape variation in the analysis. Discriminant analysis: The lambda Wilk’s test was used to compare the means of each variable among groups. For all the morphometric variables (now 12, excluding DO, BL and TL) the differences among groups or areas were significative (P< 0.01). The pre-defined areas or groups are GS (Northwest of Bay of Biscay), BB (Southeast of Bay of Biscay), GA (Galician waters), MO (Morocco) and BI (Balearic Islands). The stepwise discriminant analysis showed that the variables UJL, LS2D and LSPv did not contribute with significance to discriminant functions. Four discriminating functions were selected (Table VI).

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Table VI.- shows the standardised coefficients of the canonical discriminant functions

-.882 .481 -.568 -.509.076 .468 -.262 .802.688 -.050 .131 -.596.065 -.163 .637 -.244.653 -.391 -.254 .168.073 .653 .243 -.083

-.350 -.397 .006 -.122-.333 .065 .608 .155.268 .479 .278 .873

HLSLLJLLS1DLSPCLSAPVLPCLIOL

1 2 3 4Function

Figure 4.- Representation of the canonical discriminant functions (1) and (2).

Canonical discriminant functions

Function 1

420-2-4-6

Func

tion

2

4

2

0

-2

-4

-6

AREA

Group centroids

5 = Ireland

4 = Balearic Isl.

3 = Morocco

2 = Galicia

1= Bay of Biscay

5

4

32

1

14

The more similar areas are the pairs: GS(Southeast of Ireland) and GA (Galicia) for one side and MO (Morocco) and BB (Southest of Bay of Biscay) for other side (Fig. 4). Although statistically significative differences are found among all of them. It is also clear that M orocco, Balearic Islands and Bay of Biscay have the same value regarding discriminant function one (1) and are discriminated by function (2). Table VII

Clasification resultsa

46 1 8 3 1 593 29 4 4 14 54

11 2 38 7 2 603 4 6 46 0 591 10 2 2 41 56

78.0 1.7 13.6 5.1 1.7 100.05.6 53.7 7.4 7.4 25.9 100.0

18.3 3.3 63.3 11.7 3.3 100.05.1 6.8 10.2 78.0 .0 100.01.8 17.9 3.6 3.6 73.2 100.0

AREA1234512345

Counting

%

Original1 2 3 4 5

Forecasted belonging groupTotal

The 69.4% of original grouped cases were classified correctly.a.

From the discriminant analysis fish can be assigned to samples via cross-validation as can be seen in the table VII. The percent of “grouped” cases correctly classified is 69.4% what it is a significant level. This means that the morphometric analysis could be very useful in identifying hake populations. The areas 1 BB (Southeast of Bay of Biscay), 4 BA (Balearic Islands) and 5 GS(Northwest of Bay of Biscay) have the higher levels of discrimination with a values greater than 73%. The area in which the discrimination is lower is area 2 GA (Galicia) with a 53.7% of corrected classified cases.

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Meristic characters: The one-way analysis of variance on the meristic data show significative differences in the variable “Anal fin rays” as it can be seen in table VIII. Table VIII. General result of ANOVA on meristic characters

ANOVA

.642 3 .214 .782 .50548.943 179 .27349.585 18213.821 3 4.607 3.928 .010

209.928 179 1.173223.749 182

36.736 3 12.245 6.346 .000345.374 179 1.929382.109 182

Inter-groupsIntra-groupsTotalInter-groupsIntra-groupsTotalInter-groupsIntra-groupsTotal

DORSAL1

DORSAL2

AN

Sum ofsquares df

Quadraticmean F Sig.

The bonferroni test for multiple comparisons gave that the differences are among the area 3 MO (Morocco) and area 2 GA (Galicia), but not only with the variable AN (Anal fin ray) but also with the variable DORSAL 2 (rays of the second dorsal) for a P< 0.05. DISCUSSION: The morphometric results show significative differences among the areas, as could be expected for so distant sampling localities. These are more evident between the Mediterranean sample (BI, 4) and those from the Atlantic area (GS, 5; BB, 1; GA, 2; MO, 3), as it was also confirmed by previous genetical analysis (Roldán et al., 1998; Lundy et al., 1999). Also, differences are found among Atlantic areas being well differentiated the areas (GS, 5; GA, 2) from areas (BB, 1; MO, 3). The certain proximity observed in the morphometric results between the Galician waters and South of Ireland (although significative differences exist), seems to indicate that the environmental conditions are similar. One preliminary hypothesis could be the hidrographic conditions. For example, the south of Ireland and the Galicia present in general cooler waters than in the Bay of Biscay and different general oceanographic conditions ( Lavín et al. 1992; Varela. 1992). Also, it seems that surface waters from Northwest of Spain are more connected with Celtic Sea and the Northern of the Armorican Shelf than with the inner

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part (Southeast) of the Bay of Biscay in which the sample 1 (BB) was collected (Pingree, 1993). The water temperature could explain partially why the Bay of Biscay has a little more proximity with the morphometric results from Morocco, thus because of these warmer conditions. Other hypothesis is that exist certain degree of mixing between some of the areas selected, mainly the South of Ireland and the Galician waters. What it is evident is the differentiation between Bay of Biscay and the rest of the Northeast Atlantic areas studied: Galicia and Great Sole (South of Ireland). This shows at least that populations don’t share the same area for growing. This is a very interesting result that confirms other studies that define Galicia as a transition area among the open Atlantic-Portuguese coast and the Bay of Biscay, and others that confirms a more complex hake stock structure than the currently defined in the Northeast Atlantic area (Lundy et al., 1999; Roldán et al., 1998). At present the areas Great Sole (South of Ireland) and the Bay of Biscay (Eastern part) belong to the so called “Northern stock of hake”. Our results therefore cast doubt on the idea of Northern stock with homogeneous life history parameters (at least some of them). The meristics are less informative in this case because of its constant features among samples. However the differences found between area 2 (GA) and MO (3) are promising and they seems to reflect a latitudinal difference. It is a pity not to have the sample of Great Sole (South of Ireland), the most northern locality to confirm this hypothesis. The results from morphometric analysis have to be complemented with the results from other techniques. The problem is that in morphometric techniques significant differences can almost always be found working with large samples and many characters. Therefore ancillary information should be considered in interpreting group differences (Cadrin, 2000). In our case the sampling size and the number of variables are not too large or to many. On the other hand, our results, regarding the differences between the Atlantic and the Mediterranean hake populations have been confirmed by genetic studies (Plá et al., 1991; Roldán et al., 1998; Lundy et al., 1999). The consideration of a more complex stock structure for the so called “Northern stock” of hake (Nothern of Northeast Atlantic) was also addressed by the genetic studies using allozyme analysis (Roldán et al., 1998) and microsatellite DNA analysis (Lundy et al., 1999). ACKNOWLEDGEMENTS: Authors wish to thank to R. Gancedo, B. Patiño and C. Hernández for their work in material processing and collecting. B. Pomar, P. Torres, A. Ramos, L. Gil de Sola, P. Pereda and A. Carbonell also provided an invaluable help in collecting and sending material from

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Mediterranean Sea and African coasts. This work has been carried out under the European Union project: FAIR CT 97 3494. REFERENCES Alheit, J. and T.J. Pitcher. (1995). Hake. Fisheries, ecology and markets. Chapman and

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