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ICES CM 2004/EE:24

Morphometric variation among ( encrasicholus, L.) populations from the Bay of Biscay and Iberian waters

Bruno Caneco, Alexandra Silva, Alexandre Morais

Abstract

For management purposes, the European Atlantic anchovy is separated in two distinct stocks, one distributed in the Bay of Biscay (ICES Sub-Area VIII) and the other occupying mainly the southern part of ICES Division IXa (Bay of Cadiz). However, spatio-temporal irregularities in the dynamics of the Sub-Area VIII stock as well as scant knowledge on the IXa anchovy biology lead ICES to recommend more studies on population dynamics and possible relationships between areas. The present work describes morphometric differences between the two stocks based on the analysis of 10 samples collected within the area from Bay of Biscay to the Bay of Cadiz during two consecutive years (2000 and 2001). Distances on a “Truss Network” were computed from 2D landmark coordinates obtained from digitized images of each individual and corrected from the effect of size. Principal Component Analysis was applied to the shape data, as well as a Multidimensional Scaling to the squared Mahalonobis distances (D2) between every pair of sample centroids to visualise clustering. The significance of the computed D2 distances was also statistically tested. Finally, Artificial Neural Networks were applied to assess the robustness of sample groups highlighted in the previous analyses. Results indicate a separation between samples from the Bay of Biscay and those from Division IXa, which is stable over time, as well as a north-south cline along the Portuguese and Bay of Cadiz area. This variability is mainly caused by differences on the shape of the medium- posterior region of the body.

Keywords: Anchovy, Morphometry, Stock Identification

B. Caneco ([email protected]) , A. Silva ([email protected]) and A. Morais ([email protected]) : Instituto Nacional de Investigação Agrária e das Pescas (INIAP)-IPIMAR, Av. Brasília s/n, 1449-006 Lisboa, . Introduction

The , Engraulis encrasicholus L., is a small whose populations are widely distributed along the north-eastern and central Atlantic Ocean coasts, in the European seas (e.g. , , Azov Sea) (Whitehead et al., 1984; Whitehead et al., 1988; Plounevez and Champalbert, 1999). Having mean sizes between 12 and 18 cm, it is a spring/summer spawner which preferably eats zooplankton during its diurnal feeding activity (Matafome, 1967; Ribeiro et al., 1996; Millán, 1999; Plounevez and Champalbert, 2000). As the most valued clopeoid species on the market, it stands as an economically relevant marine resource and hence submitted to intense pressure by some important European fleets, like the Spanish purse-seiners and French pelagic trawlers (for more information on annual landings see e.g. ICES, 2001; ICES, 2003). Its population dynamics, characterized by a very short life and with spawning and catch consisting mainly of ages 1 and 2, makes its abundance largely dependent on the recruitment, which can produce rapid population changes (ICES, 2003). For management purposes, the European anchovy present in the Atlantic is separated in two distinct stock units, one distributed in the Bay of Biscay (ICES Sub-Area VIII) and the other distributed in ICES Division IXa (Portuguese coast and Bay of Cadiz), but occupying mainly the southern part of this area (Bay of Cadiz). The stock limits were essentially based on administrative considerations, and both the homogeneity of the IXa stock and the extent of mixing between the two stocks was uncertain. This led ICES Working Group (WG) to review the basis for the discrimination of these stocks along the whole European Atlantic distribution of the anchovy (ICES, 2000). The need for further studies on the dynamics of the IXa anchovy population and its possible connection with from other areas was emphasized in the WG ICES report of 2003. Catch and survey data seem to suggest the existence of an anchovy stable population in the Bay of Cadiz which may be relatively independent of the remaining populations in Division IXa (ICES, 2003). Morphometry is one of the methods used in the multidisciplinary field of stock identification (Ihssen et al., 1981). By measuring the form, morphometric studies allow to describe, analyse and understand morphological (or phenotypic) variations between populations. However, the fact that phenotypic differences between groups of fish are not necessarily associated with high genetic variability constitutes the main focus of criticism to the application of morphometrics in stock identifications studies. Swain and Foote (1999) developed an interesting discussion on the definition of phenotypic and genotypic stocks and their interactions. Some authors argue that phenotypic variation induced by the environment should be faced in a more dynamic and flexible manner and that can be seen as a good registry of the population structure in a short-term time period (Kinsey et al., 1994; Tudela, 1999). Morphometric differences can be assumed as a reflex of adaptation, delineating groups of individuals with similar growth, mortality and reproduction ratios (Rohlf, 1990, Swain and Foote, 1999). Early morphological studies on the European anchovy were focussed on the of the species, contributing to the establishment of subspecies or ‘races’ (Fage, 1911; Aleksandrov, 1927). For instance, Fage (1920) recognized two main races, the Atlantic and the Mediterranean ones, based on vertebral mean number. Recent studies have been directed to the morphological variability in populations of Mediterranean Sea and Bay of Biscay (Shevchenko, 1981; Junquera and Pérez-Gándaras, 1993; Prouzet and Metuzals, 1994; Tudela, 1999), but none of them comprised populations from whole IXa Division. The present study seeks to describe morphometric differences between the anchovy populations from the Gulf of Biscay and from Iberian waters (ICES IXa Division), as well as to assess the inter-annual stability of those variations, in order to try to better understand the anchovy stock structure in the European Atlantic area.

Material and Methods

Within the area of the South Celtic Sea to the Gulf of Cadiz, a total of 10 samples of anchovy were collected during research surveys in the spring of 2000 and 2001 (Table 1 and Figure 1). The samples were frozen just after being caught and were defrosted approximately 3 months later for laboratory analyses. According to a pilot study on the effects of cold in the fish body proportions, this freezing time period is essential to assure that all fish analysed had an equivalent fraction of body shrinkage. A digital image of the right side of each individual was obtained by a flatbed desktop scanner (Epson U9000) and biological characteristics like individual length, total and gut weight, sex, maturity stage, and fat quantity, were also recorded. Considering previous morphometric studies on the species and some specific requests, such as global coverage of the shape, clarity of recognition in the body and importance of head and fins in the distinction of clupeoid species (Bauchot and Pras, 1980), sixteen anatomic landmarks were defined to create a set of body distances forming a “Truss Network” (Strauss and Bookstein, 1982). Eye diameter and mouth length were added to the 23 distances from the truss (Figure 2). Morphometric variables were computed as Euclidean distances between the 2- D coordinates of landmarks digitized in every image. Data were examined with scatter plots of morphometric variables against fish total length and individuals with wrong measurements or incomplete data were excluded from further analysis. In total, 903 fish were analysed with sample sizes ranging from 79 to 98 individuals (Table 1).

Table 1 - Summarized information of the samples collected for morphometric analysis (area, date, geographic location and n, the final sample size).

Sample Area Date Latitude Latitude n A1 Bay of Biscay (Gironde) 24-04-2000 45º 20.3’ N 02º 08.3’ W 79 A2 Bay of Biscay (Gironde) 27-05-2001 45º 50.2’ N 03º 04.6’ W 89 B1 Bay of Biscay (Adour) 19-04-2000 44º 04.1’ N 01º 55.6’ W 82 B2 Bay of Biscay (Adour) 18-05-2001 43º 52.0’ N 01º 32.9’ W 95 C1 NW Portuguese Coast 16-03-2000 40º 30.9’ N 08º 50.1’ W 96 C2 NW Portuguese Coast 06-04-2001 40º 55.5’ N 08º 44.0’ W 90 D2 SW Portuguese Coast 11-04-2001 39º 23.9’ N 09º 18.3’ W 93 E1 Algarve (South Portugal) 31-03-2000 37º 05.3’ N 07º 27.2’ W 85 E2 Algarve (South Portugal) 20-03-2001 36º 57.2’ N 08º 03.1’ W 98 F2 Gulf of Cadiz 17-03-2001 36º 19.5’ N 06º 28.4’ W 96

Figure 1 – Locations of fishing hauls where the anchovy samples for morphometric analysis were collected. Lines define the borders of the ICES Divisions (VIIIa, VIIIb, VIIIc, VIIId, IXa).

Figure 2 - Position of anatomical landmarks (L1, …, L16 in the upper image), as well as the distances used in anchovy morphometric analysis (bottom image: black lines represent truss network design; grey lines symbolise mouth length and eye diameter).

The measurement error and the precision of each morphometric variable were quantified by repeating several times the landmark digitizing and the distance computation processes in 8 . Mean standard deviations revealed errors ranged between 0.2-0.4 mm, values acceptable for morphometric analysis according to Winans (1984). Precision decreased proportionally to the variables dimensions, which lead to the exclusion of the variable t2 (the smallest one, Figure 2) due to its high mean variance coefficient (6.9%). Assuming each sample as a group and that they all shared a common allometric pattern, log-transformed morphometric variables were corrected for the effect of size using the Burnaby correction method (Burnaby, 1966; Rohlf and Bookstein, 1987, Klingenberg, 1996). Since that the main variability present in morphometric distances is highly correlated with fish size, this technique considers the first eigenvector of the pooled within-group covariance matrix of the log-transformed morphometric variables as a fish size index. Size is removed by projecting the variables on the sub-space orthogonal to the space covered by the common size vector. A preliminary analysis was carried out to evaluate the influence of some biological parameters on size corrected (or shape) variables. The effects of maturity, fat and stomach fullness were assessed by the correlation coefficients between shape variables and gut weight. Variables t10 and t15 (Figure 2), both crossing the abdominal cavity, showed significant correlations (0.68 and 0.55, respectively) and were excluded from morphometric analysis to prevent misleading effects due to different spawning and condition stages. Gender factor was also checked using Hotteling T2 statistic to test the equality between shape mean vectors of each sex (Johnson and Wichern, 1998). Results pointed out that male and female shape was not significantly distinct and, therefore, the separation of data by gender was not justified. A total of 22 size-corrected variables (t1, t3-t4, t6-t9, t11-t22, eye diameter and mouth length) were used for the morphometric analysis, which began with a Principal Component Analysis (PCA) to explore groups of similar individuals and identify influential variables. PCA reduces the dimensionality of a data set with n measurements on p interrelated variables to a few and uncorrelated k linear combinations of these variables, called principal components (PC’s), while retaining as much as possible the variance-covariance structure of the original data set (Jolliffe, 1986). Principal components were extracted from the shape data covariance matrix. Mean values and 95% confidence ellipses of the individual scores on the first two principal components were computed for each sample. A Multidimensional Scaling was applied to the squared Mahalanobis distances (D2) between every pair of sample centroids, applying the Sammon’s (1996) non-linear mapping method for the iterative ‘stress’ minimization (Johnson and Wichern, 1998; Venables and Ripley, 2002). Using a set of similarities, this technique finds a representation of the samples in few dimensions so that the inter-samples proximities in the low-dimensional space match the original similarities. The use of Mahalanobis distance, while maximising the separation among the samples, comprises the sample variances for each variable. The two-sample Hotelling T2 statistic (Mardia et al., 1979) was used to test the significance of D2 distances, and thus the separations between the samples. Since that this analysis involved a multi-comparing process, the significant level (α) was adjusted to the number of comparisons (Zar, 1996). Artificial neural networks (ANNs) were also explored as a method of classifying anchovies according to their morphometric characteristics. This technique allows to assess the robustness of groups possibly enhanced during the previous analysis as well as to outline other groups of samples with similar shape data. The architecture of the ANNs applied in this study can be described as a three-layer feed-forward. In these networks, neurons are organized in three types of successive layers: the input layer (retains the descriptors), the hidden layer, and the output layer (holds the classification results); and are connected through a set of synapses weights which are restricted to feed-forward activations. The networks were trained by means of back-propagation learning algorithm (Rumelhart et al., 1986). During this process, the network takes every descriptor as a separate input and produces an actual output pattern according to a sigmoid transfer function. Before taking the next descriptor, it compares this output with the desired one, calculating the mean squared error (MSE). Weights are then changed until the minimization of MSE occurs. In order to accelerate training, the classic backpropagation algorithm has been improved by the momentum technique (Rumelhart et al., 1986). More detailed description and application of ANNs can be found in Rumelhart et al. (1986), Kosko (1992), Lawrence (1993), Fausset (1994) and Haralabous and Georgakarakos (1996). The ANNs implementation was performed using the commercial software “NeuroSolutions” (NeuroDimension, Inc). Diverse ANNs topologies and different sets of learning parameters have been tested in order to obtain satisfactory classification results. From the 903 examples that constitute the total number of cases a subset of 10% has been used to perform cross validation and a 20% subset used to test the network performance and classification success. A measure of the relative importance among descriptors (input morphometric variables) was performed using a “Sensitivity About the Mean” test available in the software package. This test makes possible to evaluate the network performance in response to a variation (predefined number of standard deviations) in each descriptor.

Results

The biological characteristics of the ten anchovy samples collected for the morphometric analysis are summarized in the Table 2. For the whole samples set, total length ranged between 10.5 and 19.3 cm (mean 14.2). Despite the slightly overlap of the length distributions among samples, the ones collected in the Bay of Biscay (A1, A2, B1 and B2) had bigger mean lengths than the Portuguese coast and Gulf of Biscay ones. In both years, the largest anchovies were from near Adour, with mean lengths of 16.8 cm and 15.9 cm (B1 and B2, respectively). Anchovies caught in the North-west Portugal (2000) and Algarve (2001) were the smallest ones (mean lengths close to 12.5 cm). Samples were mainly composed of spawning fish, except one from North-west Portugal (year 2000, C1) whose biological data suggest that its fishes were in a pre-spawning state. Sex ratio was, in general, balanced (50.6 % in the pooled samples), excepting off the South-west Portugal sample and one of the Algarve samples (E2), with high proportions of males. Indices of the ratio between mean weight and body weight were slightly smaller in samples from the western Portuguese coast. Fat content was low in all samples. The principal component analysis applied to the size corrected truss and additional variables showed that the two principal components (PC’s) accounted for 48% of the total variance. The first PC, accounting for 31% of total variance, was essentially a contrast between body variables (t9-t20) and head dimensions (t1-t5, t8 and DO), while PC2 (18% of total variability) basically summarised variation in the length of the base (variable t16), and small but significant contributions of variables t6, t22, t21 and CB (Figure 3a). Despite the considerable overlapping among the samples in both PC’s, it was noted that samples from Bay of Biscay (A1, A2, B1, and B2) were slightly segregated from a group formed by the Portuguese samples (C1, C2, D2, E1 and E2) (Figure 3b). In addition, fishes from Bay of Cadiz (sample F2) were separated from these two former groups. Considering the pattern of correlations between PC’s and the morphometric variables, these results indicate that fish from the Iberian area had larger heads and smaller body dimensions than the ones from Bay of Biscay. These differences were more pronounced in the Bay of Cadiz. Likewise, the Iberian samples had also greater dorsal fin base lengths.

Table 2 – Summary of biological characteristics of anchovy in each sample collected for the morphometric analysis. The percentage of spawning fish corresponds to fish in pre-spawning and spawning satges. S.d. is the standard deviation. Mean gut Mean Length Sex Ratio (% Percentage of Dominant fat Sample weight/body (cm) and (range) females) spawning fish stage weight (s.d.) A1 14.9 (12.9 - 16.8) 55.1 100 0.15 (0.04) 1 A2 14.7 (12.6 - 19.3) 58.4 100 0.14 (0.03) 1 B1 16.8 (15.3 - 18.7) 68.2 100 0.14 (0.03) 1 B2 15.9 (14.0 - 18.0) 49.5 100 0.16 (0.02) 1 C1 12.5 (10.5 - 17.5) 46.4 18 0.11 (0.02) 1 C2 13.8 (11.1 - 17.2) 54.4 100 0.11 (0.02) 1 D2 14.4 (13.0 - 15.7) 34.4 100 0.12 (0.02) 1 E1 13.6 (11.8 - 15.0) 29.4 100 0.15 (0.04) 1 E2 12.6 (11.2 - 14.6) 58.8 99 0.14 (0.02) 1 F2 13.3 (11.9 - 15.3) 52.1 100 0.14 (0.03) 1 Total 14.2 (10.5 – 19.3) 50.6 93 0.14 ( 0.03) 1

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557 314 84 772709 .2 192065128569297618111185471721619318961545812111769511665569160718773788821260613106038187061774764270 107 P t3 67 131128939705657621171341 304 0 2916584797 305 959106202468156173327 74188 - 17703655396151787811524171604211616921071281169431139 837256t27800122101 583 142301131 61840921376101926912135t216611113 1291 05 71155411791 10916317411110647141060 357 51707246 707 61 1 7381359612031 - 0. 752695945826789 137941 718 47759 77 - 5911951416168116051911 721032505112 241891 13144141 4518311201 3

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- - -0.1 0 -0.05 0.00 0.05 0.10 -0.4 -0.2 0.0 0.2 0.4 Principal Component 1 Principal Component 1

Figure 3 - Main results of the principal components analysis on the entire shape data in the first two principal components. On the left chart (a), vectors illustrate the loadings of morphometric variables on the two PC’s. In the right plot (b), are the centroids (black dots) and 95% confidence ellipses of the scores of each of the sample on the first two principal components

The multidimensional scaling on the D2 distances among the centroids of all samples (Table 3) supported the general structure highlighted by the principal component analysis. Thus, with a nearly ideal ‘stress’ (0.02), the two-dimensional representation of the dissimilarities between the samples exposed a separation of Bay of Biscay samples from the Iberian area ones (Figure 4). Regarding only the Iberian samples, it was also noticed a North-South/Western- Eastern cline (mainly in dimension 2). In other words, the segregation pattern among these samples followed their relative geographic position. The test to the significance of D2 distances among the samples challenged the indications revealed in the previous analysis. Results showed that the null hypothesis of equal samples was accepted only in one case: the comparison between the samples A2 and B2. Although the small values of D2 between some samples (e.g. A1-B1, C2-D2 and C1-C2; Table 3), all the others T2 statistics calculated for each comparison rejected the null hypothesis by laying outside the confidence limits (with α = 0,001), indicating that samples were significantly separated. Bearing in mind the results of the last analysis, the use of artificial neural networks began to consider each sampling year independently. Thus, starting with each sample as a group, some networks were trained and optimized for various topologies (i.e. different assemblages of samples) for each year data. One of the main suggestions acquired from these networks was that time factor should not affect network performances, since classification success found in analogous networks designs were similar in the two years. For that reason, and having the knowing background of the first two analyses, it was decided to eliminate the sampling year feature, combining sample data from the same area (i.e. A1+A2, B1+B2, C1+C2 and E1+E2). Networks with this new data structure were tested. The best performance was obtained with 13 hidden units and 250 training rounds (training MSE = 0.13; cross-validation MSE = 0.14). Testing predictions showed correct classifications ranging from 23% (group E) to 77% (group C). Looking to the output/desired contingency table (Table 4), almost all the misclassifications in the groups from the Bay of Biscay (both with 60% of classification success) occurred between them. Groups from Iberian areas lost some fish to the other groups of the same area. The group form Algarve (E) was the least robust, classifying more of its fish on western Portuguese coast groups than on itself. Based on these indications, the procedure advanced to the training of a network for only two groups, i.e. groups were assembled to generate a Bay of Biscay group and an Iberian group (groups F and I, respectively). Performance optimization was accomplished with 11 hidden units and 250 epochs of training (training MSE = 0.046; cross-validation MSE = 0.11). The great classification success verified in the test process (above 90%) indicates strong robustness of both groups. The sensitivity about the mean test on this last network (Figure 5) showed the significant importance of some variables from the head (t2, t5 and t7) and from the medium-posterior part of the body (t9, t17- t21). These results are analogous and support the exposed by the loadings on the first two principal components (Figure 3a), except the fin base length (t16) case which had a minor importance for the network functioning.

F2 E1 A1 B1 ion 2 E2 0246 ns e

A2 C2

Dim 2 B2 - D2

4 C1 -

6 -

-5 0 5 Dimension 1

2 F igure 4 - Results from the apply of a multidimensional scaling on Mahalanobis distances (D ) between all pair of centroid samples.

Table 3 – Mahalanobis distance between all pair of centroid samples. * indicates the only distance that was not statistically significant in the two-sample Hotteling T2 statistic test (T2 = 2,25 < conf. limit = 2,37, α = 0.001).

A1 A2 B1 B2 C1 C2 D2 E1 E2 A2 3.54 B1 2.11 3.95 B2 4.77 1.22 * 3.5 C1 14.86 13.35 17.59 15.75 C2 19.39 14.16 20.98 17.76 3.18 D2 11.34 10.05 13.5 13.82 6.23 2.52 E1 8.45 10.44 11.81 13.79 6.92 4.6 4.61 E2 10.15 10.25 14.59 12.33 5.93 4.37 4.48 3.24 F2 12.85 16.11 20.59 18.66 6.61 6.03 7.14 3 5.29

Table 4 - Classification results of the test applied to an artificial neural network trained with morphometric variables of 6 anchovies groups of samples.

Actual group No. of Predicted group membership (ouput) (desired) cases A B C D E F A 30 18 10 0 1 0 0 B 30 10 18 0 0 3 1 C 30 1 1 23 6 10 7 D 30 1 1 5 20 10 8 E 30 0 0 2 2 7 0 F 30 0 0 0 1 0 14 Percent Correct 60 60 77 67 23 47 MSE 0.10 0.01 0.09 0.13 0.12 0.08 Topology: Input layer – 22 units; Hidden layer – 13 units; Output layer – 6 units

Table 5 – Classification results of the test applied to an artificial neural network trained with morphometric variables of two anchovies groups, one composed by the Bay of Biscay samples (group F) and the other by the samples caught in Iberian area (group I). Predicted group Actual group No. of membership (ouput) (desired) cases F I F 120 111 5 I 60 9 55 Percent Correct 92 93 MSE 0.06 0.06 Topology: Input layer – 22 units; Hidden layer – 11 units; Output layer – 2 units

6 Group I 5 Group F

4 y ivit t i 3 s n

e S 2

1

0 t1 t3 t4 t5 t6 t7 t8 t9 t11 t12 t13 t14 t16 t17 t18 t19 t20 t21 t22 t23 DO CB Morphometric variable Figure 5 – Results from the sensitivity about the mean test applied to an artificial neural network trained with morphometric variables of two groups of anchovies, one composed by the Bay of Biscay samples (group F) and the other by the samples caught in Iberian area (group I).

Discussion

The data analyzed in this study showed a clear morphometric distinction between samples from Bay of Biscay and those from ICES Division IXa (also referred as Iberian), suggesting that the two currently defined stocks of European Atlantic anchovy have different morphological types. Despite the statistically significant separation discovered among almost all centroid samples, the existence and discrimination of the two morphotypes was proved by the highly accurate classifications (> 92%) of new fish provided by an artificial neural network trained with the two groups of samples. The inter-annual stability observed in samples from the same area revealed that the year of sampling did not interfere with the results. As so, one can infer that the found shape differences establish a certain pattern through time. Anchovy populations from the Division IXa area seem to have larger heads and smaller medium-posterior body proportions than those from the Bay of Biscay. The results of this study are consistent with those obtained by Junquera and Perez- Gandara (1993), who also detected morphometric differentiation between anchovies populations from north of Division IXa and populations from the Bay of Biscay, and also suggested the existence of an intermediate population in the Cantabrian area. Some other studies on the European Atlantic anchovy found two different morphological groups within the Bay of Biscay (Prouzet et al., 1989; Prouzet and Metuzals, 1994), that contradict the robust group evidenced in that area in this present study. Morphological variability along the studied area was detected in other pelagic species too, such as (Sardina pilchardus) that also showed an increasing gradient in the absolute and relative head sizes from north Atlantic to south (Silva, 2003). Shevchenko (1981) discriminate some European anchovies subspecies in the different areas of the Mediterranean Basin, in which the main morphological divergences occurred in head proportions. Morphological differences are commonly explained as a reflex of dissimilar environmental conditions (e.g. Hedgecock et al., 1984; Kinsey et al., 1994; ICES, 2003). Cadrin and Friedland (1999) stated that the true causes of morphological differences are seldom understood, due to an intricate comprehension of the interactions between phenotypic expression, environment influence and ontogeny. The genetic structure of the European Atlantic anchovy populations is scantly known. In a mitochondrial DNA analysis, Magoulas et al. (1996) mentioned that the European anchovy forms two distinct phylads, and therefore suggesting their coexistence from Bay of Biscay to Aegean Sea. Several other genetic studies have been developed in the Mediterranean area (e.g. Spanakis et al., 1988; Tudela et al., 1999), some of which presenting correspondence between genetic and morphometric differentiations (Bembo et al., 1996). At an environmental level, retention events in both Bay of Biscay and Portuguese Coast may produce some isolation between their anchovy populations. Some studies showed that the South-east part of the Bay of Biscay constitutes a key area for the Atlantic anchovy (e.g. Motos et al., 1996; Borja et al., 1996). Oceanographic features such as weak and stable oceanic circulation, relatively warm temperatures and the presence of two large with important river fresh-water outflows (Adour and Gironde) (Koutsikopoulos and le Cann, 1996) favours the establishment of an retention area in the VIIIc ICES Division. This area is delimitated by the Cantabrian cold and turbulent waters. In the Portuguese Coast, the combined effect of a low salinity plume and a poleward current during winter events creates the proper conditions for retention of egg and larvae close to the shelf break (Santos et al., 2004). Thus, the two morphotypes detected in the present work may be a result of adaptations to the environmental characteristics of the respective geographic regions, which represent distinct growth, mortality and reproduction ratios. Apart from the main separation, a morphological segregation pattern was also noticed within the Division IXa samples, which revealed to be coherent to their relative geographic position. Anchovies from the Bay of Cadiz had the greater head-to-body ratios, having shown the greater divergence from the Biscay populations. This can also reflect slight adaptative reactions to small environmental differences between the respective areas. In the west Portuguese coast the European anchovy populations are very small and contingent upon their spawning areas, which are situated in the main Portuguese estuaries (Ré, 1984; Chícharo and Teodósio, 1991; Ribeiro et al., 1996; Ré, 1996). Uriarte (1996), in his review, could not state whether the anchovy Division IXa corresponds to a single or to several small stocks. Differences found between areas in length distributions, mean weight at age and maturity ogives, indicate that the populations inhabiting Division IXa may not be entirely homogeneous, having different dynamics (ICES, 2001). This, added to the fact that anchovy is mainly concentrated in the Gulf of Cadiz, lead ICES to suggest the existence of an anchovy stable in the Bay of Cadiz relatively independent of the remaining populations of the IXa area. These other populations seem to be latent and only developing when suitable environmental conditions do take place (ICES, 2003). The relative incoherence between the significant test and the other methods observed in the present analysis seems to indicate that significant statistical differences not always mean significant dissimilarities in biological or morphological terms. Therefore, it appears to be necessary to combine the usage of significant tests with descriptive (e.g. PCA and MDS) and classification (e.g. ANNs) methods, in order to better distinguish populations in a morphometric sense. The use of artificial neural networks (ANNs) on morphometrics is mainly applied on molecular biology and human medicine fields. Its predictive ability has not been used often in populations, species or stocks identifications approaches. However, some of the few examples can be seen in Culverhouse et al. (1994) and Jonker et al. (2000). Several recent studies have shown that, for the classification purpose, ANNs often have higher predictive performance than the conventional statistical procedures, such as Discriminant Analysis and logistic regression (Edwards and Morse, 1995; Lek et al., 1996; Simmonds et al., 1996; Haralabous and Georgakarakos, 1996; Manel et al., 1999). On the whole, the analysis developed in this study supported the separation of the European Atlantic anchovy in two distinct stock units, also showing morphometric variability in the IXa Division stock. These results must also be confirmed through genetic techniques, which are already in progress. During the current laboratory analysis, biological material was also collected for a subsequent genetic analysis. Further studies on European Atlantic stocks and population structures should comprise samples from adjacent areas. For instance, Northwest Africa should be included to investigate whether populations mingle with the population inhabiting the Bay of Cadiz

Acknowledgements

The authors wish to thank J. Massé (IFREMER) for collaboration in the samples collection (as part of the EU Study PELASSES 080/99), M. Garção for laboratory assistance, Y. Stratoudakis for his valuable comments and reviews, and L.Martins for her precious support during the writing process. The work was supported by the PELAGICOS project (financed by the Portuguese Ministry of Science and Higher Education and affiliated to SPACC).

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