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Morphological study of Diplodus sargus, Diplodus puntazzo, and Lithognathus mormyrus (Sparidae) in the Eastern Atlantic and Mediterranean Sea
Fisheries Research 57 (2002) 1–8
Morphological study of Diplodus sargus, Diplodus puntazzo, and Lithognathus mormyrus (Sparidae) in the Eastern Atlantic and Mediterranean Sea J. Palma*, J.P. Andrade UCTRA/CCMAR, Universidade do Algarve, FCMA, Campus de Gambelas, 8000-810 Faro, Portugal Received 21 November 2000; received in revised form 14 May 2001; accepted 3 July 2001
Abstract Truss morphometric measurements were made on white seabream, Diplodus sargus, sharpsnout seabream, Diplodus puntazzo, and striped seabream, Lithognathus mormyrus from Portugal, Spain, Italy and Greece. Univariate and multivariate statistical analyses were used to discriminate populations. Stepwise discriminant analysis yielded a reduced variable set that identified significant differences between all the four countries. The overall percent-correct classification rate for the four samples from the stepwise discriminant analysis based on 7, 9 and 10 adjusted morphometric characters (D. sargus, D. puntazzo, L. mormyrus samples, respectively), was 93.7, 90.6 and 96.3%. For each species, there was a significant degree of morphological dissimilarity between countries. A clear geographical gradient occurs between the Atlantic and the Mediterranean samples, suggesting that in some cases the fish from these areas represent separate groups. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Diplodus sargus; Diplodus puntazzo; Lithognathus mormyrus; Morphometric; Truss; Population discrimination
1. Introduction The quantification of specific characteristics of an individual, or a group of individuals, can demonstrate the degree of speciation induced by both biotic and abiotic conditions, and contribute to the definition of different stocks of species. The concept of geographical structure in fish populations is rather fundamental for population dynamics and management of fisheries (Bailey, 1997), so that the identification of the geographical ranges of each stock unit becomes an important issue (Ihssen et al., 1981). *
Corresponding author. Tel.: þ351-289-800-953; fax: þ351-289-818-353. E-mail address: [email protected] (J. Palma).
Multivariate statistical analysis of morphometric characters is a powerful technique to investigate the geographical variation of stocks. Because it can yield information complementary to that derived from biochemical, physiological and life history studies, it has provided useful results for assessing the stock structure of several species of marine fish (Schaefer, 1989). The landmark-based techniques of geometric morphometrics pose no restrictions on the directions of variation and localization of changes in shape, and are very effective in capturing information about the shape of an organism (Cavalcanti et al., 1999). More recently, the image analysis systems played a major role in the development of morphometric techniques, boosting the utility of morphometric research in the fish stock identification (Cadrin and Friedland, 1999).
0165-7836/02/$ – see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 5 - 7 8 3 6 ( 0 1 ) 0 0 3 3 5 - 6
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J. Palma, J.P. Andrade / Fisheries Research 57 (2002) 1–8
Table 1 Sample location and number of specimens used in the truss protocol Country
Capture location
D. sargus
D. puntazzo
L. mormyrus
Portugal Spain Italy Greece
South coast (Algarve) Mediterranean (Alicante) Trieste (North Adriatic) Crete Island
114 30 33 30
35 41 41 43
123 32 31 31
No relevant information on stock definition has been produced recently concerning sparids, which are highly important fish in the Atlantic and Mediterranean. Relying in the truss morphometric protocol, this paper aims to identify differences between the populations of Diplodus sargus (L.), Diplodus puntazzo (Cetti), Lithognathus mormyrus (L.) along the Portuguese coast, and in the Mediterranean Sea.
2. Material and methods 2.1. Sampling Samples of D. sargus (n ¼ 207; range size: 130– 290 mm), D. puntazzo (n ¼ 160; range size: 147–
397 mm) and L. mormyrus (n ¼ 189; range size: 120–288 mm), were collected from four countries: Portugal, Spain, Italy and Greece (Table 1). Fish were X-rayed (intensity between 25 and 27 kV), with the fins in the extended position. The resulting images maintained the real size of fish. 2.2. Truss measurements The truss morphometrics protocol (Strauss and Bookstein, 1982; Bookstein et al., 1985) has been used to describe the shape of each fish using a set of morphometric characters. This protocol prevents biases, which occur with traditional procedures, because it gives redundant measurements for certain aspects of the body morphology. Morphometric
Fig. 1. Location of the 14 landmarks used to calculate the truss networks (lines).
J. Palma, J.P. Andrade / Fisheries Research 57 (2002) 1–8
measurements were made on a digitalizing table WACOM Ultra Pad, using the Image Pro Plus 3.0 software. For each specimen, 31 linear measurements and six areas were taken between the 14 landmark points (Fig. 1) on the left side of the fish. The landmarks were in clockwise direction: (1) anterior tip of
3
the snout; (2) dorsal surface of the head at the nearest point to the eye globe; (3) posterior point of the neurocranium; (4) origin of the dorsal fin; (5) origin of the 8th ray of the dorsal fin; (6) origin of the 16th ray of the dorsal fin; (7) posterior end of the dorsal fin; (8) dorsal origin of the caudal fin; (9) ventral origin of
Table 2 Loadings from principal component analysis of the 37 morphometric characters for D. sargus, D. puntazzo and L. mormyrus samplesa Descriptor
D. sargus PC1
D. puntazzo PC2
Ar1 Ar2 Ar3 Ar4 Ar5 Ar6 A1 A2 A3 A4 A5 A6 B1 B2 B3 B4 B5 C1 C2 C3 C4 C5 D1 D2 D3 D4 D5 E1 E2 E3 E4 E5 F1 F2 F3 F4 F5
0.951 0.978 0.982 0.989 0.954 0.953 0.901 0.901 0.402 0.947 0.964 0.938 0.857 0.926 0.992 0.981 0.979 0.020 0.960 0.990 0.991 0.986 0.940 0.947 0.984 0.991 0.990 0.960 0.772 0.969 0.952 0.982 0.785 0.810 0.921 0.937 0.949
0.134 0.146 0.001 0.041 0.234 0.228 0.132 0.262 0.804 0.160 0.057 0.051 0.186 0.289 0.052 0.003 0.076 0.245 0.049 0.035 0.062 0.031 0.053 0.089 0.116 0.078 0.000 0.148 0.395 0.046 0.210 0.110 0.338 0.345 0.253 0.212 0.160
Explained variance % of total
31.40 84.9
1.74 4.7
a
PC1 0.934 0.982 0.979 0.983 0.968 0.954 0.896 0.875 0.731 0.908 0.949 0.905 0.765 0.934 0.975 0.980 0.986 0.829 0.919 0.674 0.977 0.979 0.897 0.134 0.823 0.984 0.980 0.958 0.862 0.912 0.951 0.985 0.793 0.739 0.928 0.891 0.909 29.95 80.9
L. mormyrus PC2 0.277 0.083 0.042 0.050 0.157 0.013 0.268 0.270 0.239 0.202 0.148 0.275 0.411 0.004 0.032 0.068 0.012 0.079 0.087 0.093 0.049 0.013 0.011 0.742 0.242 0.053 0.066 0.085 0.232 0.161 0.188 0.073 0.002 0.161 0.024 0.066 0.056 1.43 3.7
Loadings are listed for the two principal components. Characters descriptors refer to Fig. 1.
PC1 0.956 0.873 0.967 0.976 0.965 0.978 0.921 0.901 0.748 0.959 0.963 0.933 0.806 0.793 0.983 0.984 0.986 0.011 0.907 0.975 0.986 0.966 0.901 0.521 0.967 0.983 0.964 0.966 0.769 0.958 0.942 0.974 0.873 0.846 0.943 0.955 0.962 30.64 82.8
PC2 0.148 0.181 0.001 0.004 0.138 0.026 0.079 0.059 0.459 0.149 0.107 0.136 0.311 0.321 0.039 0.012 0.024 0.660 0.076 0.091 0.022 0.077 0.022 0.082 0.024 0.007 0.022 0.062 0.382 0.026 0.150 0.012 0.023 0.137 0.184 0.016 0.020 1.24 3.3
4
J. Palma, J.P. Andrade / Fisheries Research 57 (2002) 1–8
Table 3 PCS, generalized Mahalanobis distances (D2), and the F-statistics for the linear discriminant functions for all truss characters for D. sargus, D. puntazzo and L. mormyrus from the four sampling places
the dorsal fin; (10) insertion of the anal fin; (11) origin of the anal fin; (12) insertion of the pelvic fin; (13) origin of the pelvic fin; (14) posterior insertion of the sub-operculum.
Country
2.3. Data analysis
Statistic
D. sargus Portugal PCS D2 F Spain
Italy
Greece
Italy
Greece
15.88 8.72 54.89*** 18.39***
90.9 – –
PCS D2 F
31.4 7.53 68.46*** 15.15***
12.17 25.66***
Greece
**
92.7 – –
PCS D2 F
34.01 66.03**
15.92 33.57**
87.8 – –
PCS D2 F
41.27 81.75**
23.02 49.72**
7.18 15.5***
PCS D2 F
96.7 – –
88.4 – –
97.6 – – 17.78 42.09** 12.5 28.84** 44.05 101.6**
93.8 – – 9.16 13.37***
93.5 – –
23.21 33.88**
39.01 56.25**
The morphometric data were transformed to common logarithms in order to increase linearity and multivariate normality (Pimentel, 1979). Outliers were detected by regression analyses of morphometric characters against the standard length and by scatter plots of residuals vs. predicted values (Cook and Weisburg, 1982). Size-dependent variation was removed using an allometric approach (Reist, 1985). Data were transformed using the formula Mtrans ¼ log M bðlog SL log SLmean Þ where Mtrans is the transformed measurement, M the original measurement, b the within-group slope regressions of the log M vs. log SL, SL the standard length of the fish, and SLmean the overall mean of the standard length. As a first approach, principal components analysis (PCA) was used: (i) to analyse the separation between populations through the matrix variability; (ii) to identify colinear variables; (iii) to choose the right subset of variables to be used in the discriminant
94.3 – – 28.8 55.8**
P < 0:001. P < 0:0001.
***
90 – –
PCS D2 F
PCS D2 F
Greece
94.7 – –
PCS D2 F
PCS D2 F
Italy
Italy
15.08 48.25***
L. mormyrus Portugal PCS D2 F Spain
Spain
PCS D2 F
D. puntazzo Portugal PCS D2 F Spain
Portugal
96.8 – –
Table 4 Means of canonical variables for D. sargus, D. puntazzo and L. mormyrus samples (CV: canonical variables) Samples
CV1
CV2
CV3
D. sargus Portugal Spain Italy Greece
1.674 1.842 1.702 2.648
0.104 0.361 1.918 1.355
0.051 1.485 0.342 0.914
D. puntazzo Portugal Spain Italy Greece
3.993 0.477 1.499 2.276
1.314 2.642 0.312 1.152
0.043 0.309 1.390 0.996
L. mormyrus Portugal Spain Italy Greece
1.635 1.622 0.069 4.883
0.647 1.671 2.248 1.402
0.054 1.254 1.144 0.377
J. Palma, J.P. Andrade / Fisheries Research 57 (2002) 1–8
analysis, which verified the level of distinction between samples. An Equamax rotation was used. Correlations between the five principal components (PC1–PC5) and the SL were evaluated with Pearson’s product-moment correlation. After checking for homoscedasticity of the variance–covariance matrices, particular subsets of variables were evaluated on the basis of: (i) the percentage of correct re-classification in discriminant analysis; (ii) the Mahalanobis distance between centroids and its associated probability; (iii) the values of Wilk’s l; (iv) the consistency of the variable selection by stepwise and backward procedures. A multivariate variance analysis of the canonical variable obtained from the previous sequence of analysis was used to evaluate the spatial separation between the centroids of the four samples (Johnson and Wichern, 1982). Statistical differences were considered significant where P 0:05.
3. Results A total of 89.6, 84.6 and 86.1% of the total variation associated with the 37 morphometric characters was accounted for the first two principal components for
5
D. sargus, D. puntazzo and L. mormyrus, respectively. PC1 loadings explained 31.4, 29.95 and 30.64% of the total variation for D. sargus, D. puntazzo and L. mormyrus, respectively (Table 2). PC1 loadings were similar in size and sign indicating it was a general measure of fish size for all three species (Bookstein et al., 1985). Only a small number of measurements did not correlate with PC1 (A3 and C1 in D. sargus, C3 in D. puntazzo and D2 and C1 in L. mormyrus), correlating with PC2. Stepwise discriminant function analysis on truss data for the three species indicated highly significant differences on the truss element distances among the different countries (D. sargus: Wilk’s l ¼ 0:07838, F ¼ 38:481, P < 0:00001; D. puntazzo: Wilk’s l ¼ 0:02301, F ¼ 42:297, P < 0:00001; L. mormyrus: Wilk’s l ¼ 0:04001, F ¼ 39:641, P < 0:00001). Seven truss elements were selected for the white seabream (Ar1, Ar6, A3, A6, B5, D2 and F3), nine for the sharpsnout sea bream (Ar1, Ar5, A2, A5, A6, B1, C1, D4 and F3) and 10 for the striped sea bream (Ar3, A1, A2, A6, B2, B3, C5, D1, F2, F3). The head related characteristics represented 57.1, 55.5 and 30% of the characteristics found as being different for D. sargus, D. puntazzo and L. mormyrus, respectively.
Fig. 2. Bivariate mean 95% confidence ellipsoids for the canonical discriminant-factor scores for white seabream (D. sargus): Portugal (*); Spain (D); Italy (^); Greece (&).
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J. Palma, J.P. Andrade / Fisheries Research 57 (2002) 1–8
Fig. 3. Bivariate mean 95% confidence ellipsoids for the canonical discriminant-factor scores for sharpsnout seabream (D. puntazzo): Portugal (*); Spain (D); Italy (^); Greece (&).
Fig. 4. Bivariate mean 95% confidence ellipsoids for the canonical discriminant-factor scores for striped seabream (L. mormyrus): Portugal (*); Spain (D); Italy (^); Greece (&).
J. Palma, J.P. Andrade / Fisheries Research 57 (2002) 1–8
Percent classification success (PCS) for each sample, the generalized Mahalanobis distances (D2) and F-statistics for the three species are presented in Table 3. There is also a reasonable degree of distinction between samples with a constant separation of the Portuguese sample from those of Spain, Italy and Greece, as shown by the mean values from the canonical analysis (Table 4). The Portuguese samples always correlated negatively with CV1, while no distinct pattern was found for the other samples. D. sargus (Fig. 2) and D. puntazzo (Fig. 3) presented a geographical gradient, which was not the case for L. mormyrus (Fig. 4).
4. Discussion Many nongenetic markers have often been used to understand the population structure and the movement of fish (Garcia de Leo´ n et al., 1997), though it is not easy to determine the correct spatial scale to employ in identifying populations of fish (Larkin, 1981). The presence of natural geographical barriers contribute largely to the emergence of differences between populations. A phylogeographic break due to the Straits of Gibraltar has been referred to by several authors (e.g. Borsa et al., 1997; Naciri et al., 1999), and we confirm that a difference exists between the Atlantic and Mediterranean populations for the three species studied. Although sharing the same habitats, D. sargus and D. puntazzo (Macpherson et al., 1997) have dissimilar morphological characteristics. Differences between populations of D. sargus are based on a smaller number of morphological characteristics than for those of D. puntazzo. In both cases, more than 50% of the characteristics are head-related, which suggests the influence of habitat differences between the populations. In fact, feeding is a well-known factor that influences head morphology (e.g. Gerking, 1994; Hyndes et al., 1997; Delariva and Agostinho, 2001). Thus, if different populations of a same species show discordant pattern of head morphology, this is often due to the exploitation of different ecological niches, specially constrained by the availability and type of prey. These differences are based on orographic and physical characteristics of each habitat (e.g. water temperature,
7
currents) induced by geographical dissimilarities. Similar results regarding the head morphology were obtained by Sara` et al. (1999) on cultivated D. puntazzo reared under different conditions. When genetically analysed (Bargelloni, unpublished data), these same samples produced similar results, with higher degree of homogeneity for D. sargus than for D. puntazzo and L. mormyrus. Furthermore, this study also found a genetic differentiation between the Atlantic and Mediterranean samples. A similar constraint, although with a lower significant differentiation, appeared between the East and eastern Mediterranean samples. Lenfant and Planes (1996) also refer to a genetic heterogeneity between samples of D. sargus from the Lion’s Gulf (France) and the Ligurian Sea (Italy). According to these authors, the break in the costal continuum and the complexity of local current systems, which may retard larval dispersal, could determine the degree of heterogeneity referred to above. Further studies on the ecological characteristics of this species could certainly assign the observed differentiation between populations. In the case of L. mormyrus, similar constraint for differentiation such as head morphology found for D. sargus and D. Puntazzo cannot be applied, since only 30% of the differentiating characteristics are head-related. Morphometric studies have been able to identify differences emphasizing them as helpful tools for the discrimination of fish populations. Truss analysis combined with image analysis is a step ahead to produce a better understanding of stock structuring of fish species. Acknowledgements This work was funded by the EC project AIR3CT94-1926. The work of Jorge Palma was funded by a Ph.D. Grant PRAXIS XXI/9441/96. The authors thank Dr. Marta Linde for her help during sampling of the Spanish sample. The manuscript benefited from the comments of two anonymous referees. References Bailey, K.M., 1997. Structural dynamics and ecology of flatfish populations. J. Sea Res. 37, 269–280.
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Bookstein, F.L., Chernoff, B., Elder, R.L., Humphries, J.M., Smith, G.R., Strauss, R.E., 1985. Morphometrics in evolutionary biology, the geometry of size and shape with examples from fish. Acad. Natl. Sci. Philadelphia Spec. Pub. 15, 277. Borsa, P., Blanquer, A., Berrebi, P., 1997. Genetic structure of flounders Platichthys flesus and P. stellatus at different geographic scales. Mar. Biol. 129 (2), 233–246. Cadrin, S.H., Friedland, K.D., 1999. The utility of image processing techniques for morphometric analysis and stock identification. Fish. Res. 43, 129–139. Cavalcanti, M.J., Monteiro, L.R., Lopes, P.R.D., 1999. Landmarkbased morphometric analysis in selected species of Serranid fishes (Perciformes: Teleostei). Zool. Stud. 38 (3), 287–294. Cook, R.D., Weisburg, S., 1982. Residuals and Influence in Regression. Chapman & Hall, New York. Delariva, R.L., Agostinho, A.A., 2001. Relationship between morphology and diets of six neotropical loricariids. J. Fish Biol. 58, 832–847. Garcia de Leo´ n, F.J., Chikhi, L., Bonhomme, F., 1997. Microsatellite polymorphism and population subdivision in natural populations of European sea bass Dicentrarchus labrax (Linnaeus, 1758). Mol. Ecol. 6, 51–62. Gerking, S.D., 1994. Feeding Ecology of Fish. Academic Press, San Diego, CA. Hyndes, G.A., Platell, M.E., Potter, I.C., 1997. Relationships between diet and body size, mouth morphology, habitat and movement of six sillaginid species in coastal waters: implications for resource partitioning. Mar. Biol. 128, 585–598. Ihssen, P.E., Booke, H.E., McGlade, J.M., Payne, N.R., Utter, F.M., 1981. Stock identification: materials and methods. Can. J. Fish. Aquat. Sci. 38, 1838–1855.
Johnson, R.A., Wichern, D.W., 1982. Applied Multivariate Statistical Analysis. Prentice-Hall, Englewood Cliffs, NJ. Larkin, P.A., 1981. A perspective on population genetics and salmon management. Can. J. Fish. Aquat. Sci. 38, 1469– 1475. Lenfant, P., Planes, S., 1996. Genetic differentiation of white sea bream within the Lion’s Gulf and the Ligurian Sea (Mediterranean Sea). J. Fish Biol. 49, 613–621. Macpherson, E., Biagi, F., Francour, P., Garcia Rubies, A., Harmelin, J.G., Harmelin, V.M.L., Jouvenel, J.Y., Planes, S., Vigliola, L., Tunesi, L., 1997. Mortality of juvenile fishes of the genus Diplodus in protected and unprotected areas in the western Mediterranean Sea. Mar. Ecol. Prog. Ser. 160, 135– 147. Naciri, M., Lemaire, C., Borsa, P., Bonhomme, F., 1999. Genetic study of the Atlantic/Mediterranean transition in the sea bass (Dicentrarchus labrax). J. Hered. 90 (6), 591–596. Pimentel, R.A., 1979. Morphometrics the Multivariate Analysis of Biological Data. Kendall/Hunt Publ., Dubuque. Reist, J.D., 1985. An empirical evaluation of several univariate methods that adjust for size variation in morphometric data. Can. J. Zool. 63, 1429–1439. Sara`, M., Favarolo, E., Mazzola, A., 1999. Comparative morphometrics of sharpsnout seabream (Diplodus puntazzo Cetti, 1777), reared in different conditions. Aquacult. Eng. 19, 195– 209. Schaefer, K.M., 1989. Morphometric analysis of Yellowfin tuna Thunnus albacares from the Pacific Ocean. Inter-Amer. Trop. Tuna Commition 19 (5), 389–427. Strauss, R.E., Bookstein, F.L., 1982. The truss: body from reconstructions in morphometrics. Syst. Zool. 31, 113–135.
Morphological study of Diplodus sargus, Diplodus puntazzo, and Lithognathus mormyrus (Sparidae) in the Eastern Atlantic and Mediterranean Sea J. Palma*, J.P. Andrade UCTRA/CCMAR, Universidade do Algarve, FCMA, Campus de Gambelas, 8000-810 Faro, Portugal Received 21 November 2000; received in revised form 14 May 2001; accepted 3 July 2001
Abstract Truss morphometric measurements were made on white seabream, Diplodus sargus, sharpsnout seabream, Diplodus puntazzo, and striped seabream, Lithognathus mormyrus from Portugal, Spain, Italy and Greece. Univariate and multivariate statistical analyses were used to discriminate populations. Stepwise discriminant analysis yielded a reduced variable set that identified significant differences between all the four countries. The overall percent-correct classification rate for the four samples from the stepwise discriminant analysis based on 7, 9 and 10 adjusted morphometric characters (D. sargus, D. puntazzo, L. mormyrus samples, respectively), was 93.7, 90.6 and 96.3%. For each species, there was a significant degree of morphological dissimilarity between countries. A clear geographical gradient occurs between the Atlantic and the Mediterranean samples, suggesting that in some cases the fish from these areas represent separate groups. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Diplodus sargus; Diplodus puntazzo; Lithognathus mormyrus; Morphometric; Truss; Population discrimination
1. Introduction The quantification of specific characteristics of an individual, or a group of individuals, can demonstrate the degree of speciation induced by both biotic and abiotic conditions, and contribute to the definition of different stocks of species. The concept of geographical structure in fish populations is rather fundamental for population dynamics and management of fisheries (Bailey, 1997), so that the identification of the geographical ranges of each stock unit becomes an important issue (Ihssen et al., 1981). *
Corresponding author. Tel.: þ351-289-800-953; fax: þ351-289-818-353. E-mail address: [email protected] (J. Palma).
Multivariate statistical analysis of morphometric characters is a powerful technique to investigate the geographical variation of stocks. Because it can yield information complementary to that derived from biochemical, physiological and life history studies, it has provided useful results for assessing the stock structure of several species of marine fish (Schaefer, 1989). The landmark-based techniques of geometric morphometrics pose no restrictions on the directions of variation and localization of changes in shape, and are very effective in capturing information about the shape of an organism (Cavalcanti et al., 1999). More recently, the image analysis systems played a major role in the development of morphometric techniques, boosting the utility of morphometric research in the fish stock identification (Cadrin and Friedland, 1999).
0165-7836/02/$ – see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 5 - 7 8 3 6 ( 0 1 ) 0 0 3 3 5 - 6
2
J. Palma, J.P. Andrade / Fisheries Research 57 (2002) 1–8
Table 1 Sample location and number of specimens used in the truss protocol Country
Capture location
D. sargus
D. puntazzo
L. mormyrus
Portugal Spain Italy Greece
South coast (Algarve) Mediterranean (Alicante) Trieste (North Adriatic) Crete Island
114 30 33 30
35 41 41 43
123 32 31 31
No relevant information on stock definition has been produced recently concerning sparids, which are highly important fish in the Atlantic and Mediterranean. Relying in the truss morphometric protocol, this paper aims to identify differences between the populations of Diplodus sargus (L.), Diplodus puntazzo (Cetti), Lithognathus mormyrus (L.) along the Portuguese coast, and in the Mediterranean Sea.
2. Material and methods 2.1. Sampling Samples of D. sargus (n ¼ 207; range size: 130– 290 mm), D. puntazzo (n ¼ 160; range size: 147–
397 mm) and L. mormyrus (n ¼ 189; range size: 120–288 mm), were collected from four countries: Portugal, Spain, Italy and Greece (Table 1). Fish were X-rayed (intensity between 25 and 27 kV), with the fins in the extended position. The resulting images maintained the real size of fish. 2.2. Truss measurements The truss morphometrics protocol (Strauss and Bookstein, 1982; Bookstein et al., 1985) has been used to describe the shape of each fish using a set of morphometric characters. This protocol prevents biases, which occur with traditional procedures, because it gives redundant measurements for certain aspects of the body morphology. Morphometric
Fig. 1. Location of the 14 landmarks used to calculate the truss networks (lines).
J. Palma, J.P. Andrade / Fisheries Research 57 (2002) 1–8
measurements were made on a digitalizing table WACOM Ultra Pad, using the Image Pro Plus 3.0 software. For each specimen, 31 linear measurements and six areas were taken between the 14 landmark points (Fig. 1) on the left side of the fish. The landmarks were in clockwise direction: (1) anterior tip of
3
the snout; (2) dorsal surface of the head at the nearest point to the eye globe; (3) posterior point of the neurocranium; (4) origin of the dorsal fin; (5) origin of the 8th ray of the dorsal fin; (6) origin of the 16th ray of the dorsal fin; (7) posterior end of the dorsal fin; (8) dorsal origin of the caudal fin; (9) ventral origin of
Table 2 Loadings from principal component analysis of the 37 morphometric characters for D. sargus, D. puntazzo and L. mormyrus samplesa Descriptor
D. sargus PC1
D. puntazzo PC2
Ar1 Ar2 Ar3 Ar4 Ar5 Ar6 A1 A2 A3 A4 A5 A6 B1 B2 B3 B4 B5 C1 C2 C3 C4 C5 D1 D2 D3 D4 D5 E1 E2 E3 E4 E5 F1 F2 F3 F4 F5
0.951 0.978 0.982 0.989 0.954 0.953 0.901 0.901 0.402 0.947 0.964 0.938 0.857 0.926 0.992 0.981 0.979 0.020 0.960 0.990 0.991 0.986 0.940 0.947 0.984 0.991 0.990 0.960 0.772 0.969 0.952 0.982 0.785 0.810 0.921 0.937 0.949
0.134 0.146 0.001 0.041 0.234 0.228 0.132 0.262 0.804 0.160 0.057 0.051 0.186 0.289 0.052 0.003 0.076 0.245 0.049 0.035 0.062 0.031 0.053 0.089 0.116 0.078 0.000 0.148 0.395 0.046 0.210 0.110 0.338 0.345 0.253 0.212 0.160
Explained variance % of total
31.40 84.9
1.74 4.7
a
PC1 0.934 0.982 0.979 0.983 0.968 0.954 0.896 0.875 0.731 0.908 0.949 0.905 0.765 0.934 0.975 0.980 0.986 0.829 0.919 0.674 0.977 0.979 0.897 0.134 0.823 0.984 0.980 0.958 0.862 0.912 0.951 0.985 0.793 0.739 0.928 0.891 0.909 29.95 80.9
L. mormyrus PC2 0.277 0.083 0.042 0.050 0.157 0.013 0.268 0.270 0.239 0.202 0.148 0.275 0.411 0.004 0.032 0.068 0.012 0.079 0.087 0.093 0.049 0.013 0.011 0.742 0.242 0.053 0.066 0.085 0.232 0.161 0.188 0.073 0.002 0.161 0.024 0.066 0.056 1.43 3.7
Loadings are listed for the two principal components. Characters descriptors refer to Fig. 1.
PC1 0.956 0.873 0.967 0.976 0.965 0.978 0.921 0.901 0.748 0.959 0.963 0.933 0.806 0.793 0.983 0.984 0.986 0.011 0.907 0.975 0.986 0.966 0.901 0.521 0.967 0.983 0.964 0.966 0.769 0.958 0.942 0.974 0.873 0.846 0.943 0.955 0.962 30.64 82.8
PC2 0.148 0.181 0.001 0.004 0.138 0.026 0.079 0.059 0.459 0.149 0.107 0.136 0.311 0.321 0.039 0.012 0.024 0.660 0.076 0.091 0.022 0.077 0.022 0.082 0.024 0.007 0.022 0.062 0.382 0.026 0.150 0.012 0.023 0.137 0.184 0.016 0.020 1.24 3.3
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J. Palma, J.P. Andrade / Fisheries Research 57 (2002) 1–8
Table 3 PCS, generalized Mahalanobis distances (D2), and the F-statistics for the linear discriminant functions for all truss characters for D. sargus, D. puntazzo and L. mormyrus from the four sampling places
the dorsal fin; (10) insertion of the anal fin; (11) origin of the anal fin; (12) insertion of the pelvic fin; (13) origin of the pelvic fin; (14) posterior insertion of the sub-operculum.
Country
2.3. Data analysis
Statistic
D. sargus Portugal PCS D2 F Spain
Italy
Greece
Italy
Greece
15.88 8.72 54.89*** 18.39***
90.9 – –
PCS D2 F
31.4 7.53 68.46*** 15.15***
12.17 25.66***
Greece
**
92.7 – –
PCS D2 F
34.01 66.03**
15.92 33.57**
87.8 – –
PCS D2 F
41.27 81.75**
23.02 49.72**
7.18 15.5***
PCS D2 F
96.7 – –
88.4 – –
97.6 – – 17.78 42.09** 12.5 28.84** 44.05 101.6**
93.8 – – 9.16 13.37***
93.5 – –
23.21 33.88**
39.01 56.25**
The morphometric data were transformed to common logarithms in order to increase linearity and multivariate normality (Pimentel, 1979). Outliers were detected by regression analyses of morphometric characters against the standard length and by scatter plots of residuals vs. predicted values (Cook and Weisburg, 1982). Size-dependent variation was removed using an allometric approach (Reist, 1985). Data were transformed using the formula Mtrans ¼ log M bðlog SL log SLmean Þ where Mtrans is the transformed measurement, M the original measurement, b the within-group slope regressions of the log M vs. log SL, SL the standard length of the fish, and SLmean the overall mean of the standard length. As a first approach, principal components analysis (PCA) was used: (i) to analyse the separation between populations through the matrix variability; (ii) to identify colinear variables; (iii) to choose the right subset of variables to be used in the discriminant
94.3 – – 28.8 55.8**
P < 0:001. P < 0:0001.
***
90 – –
PCS D2 F
PCS D2 F
Greece
94.7 – –
PCS D2 F
PCS D2 F
Italy
Italy
15.08 48.25***
L. mormyrus Portugal PCS D2 F Spain
Spain
PCS D2 F
D. puntazzo Portugal PCS D2 F Spain
Portugal
96.8 – –
Table 4 Means of canonical variables for D. sargus, D. puntazzo and L. mormyrus samples (CV: canonical variables) Samples
CV1
CV2
CV3
D. sargus Portugal Spain Italy Greece
1.674 1.842 1.702 2.648
0.104 0.361 1.918 1.355
0.051 1.485 0.342 0.914
D. puntazzo Portugal Spain Italy Greece
3.993 0.477 1.499 2.276
1.314 2.642 0.312 1.152
0.043 0.309 1.390 0.996
L. mormyrus Portugal Spain Italy Greece
1.635 1.622 0.069 4.883
0.647 1.671 2.248 1.402
0.054 1.254 1.144 0.377
J. Palma, J.P. Andrade / Fisheries Research 57 (2002) 1–8
analysis, which verified the level of distinction between samples. An Equamax rotation was used. Correlations between the five principal components (PC1–PC5) and the SL were evaluated with Pearson’s product-moment correlation. After checking for homoscedasticity of the variance–covariance matrices, particular subsets of variables were evaluated on the basis of: (i) the percentage of correct re-classification in discriminant analysis; (ii) the Mahalanobis distance between centroids and its associated probability; (iii) the values of Wilk’s l; (iv) the consistency of the variable selection by stepwise and backward procedures. A multivariate variance analysis of the canonical variable obtained from the previous sequence of analysis was used to evaluate the spatial separation between the centroids of the four samples (Johnson and Wichern, 1982). Statistical differences were considered significant where P 0:05.
3. Results A total of 89.6, 84.6 and 86.1% of the total variation associated with the 37 morphometric characters was accounted for the first two principal components for
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D. sargus, D. puntazzo and L. mormyrus, respectively. PC1 loadings explained 31.4, 29.95 and 30.64% of the total variation for D. sargus, D. puntazzo and L. mormyrus, respectively (Table 2). PC1 loadings were similar in size and sign indicating it was a general measure of fish size for all three species (Bookstein et al., 1985). Only a small number of measurements did not correlate with PC1 (A3 and C1 in D. sargus, C3 in D. puntazzo and D2 and C1 in L. mormyrus), correlating with PC2. Stepwise discriminant function analysis on truss data for the three species indicated highly significant differences on the truss element distances among the different countries (D. sargus: Wilk’s l ¼ 0:07838, F ¼ 38:481, P < 0:00001; D. puntazzo: Wilk’s l ¼ 0:02301, F ¼ 42:297, P < 0:00001; L. mormyrus: Wilk’s l ¼ 0:04001, F ¼ 39:641, P < 0:00001). Seven truss elements were selected for the white seabream (Ar1, Ar6, A3, A6, B5, D2 and F3), nine for the sharpsnout sea bream (Ar1, Ar5, A2, A5, A6, B1, C1, D4 and F3) and 10 for the striped sea bream (Ar3, A1, A2, A6, B2, B3, C5, D1, F2, F3). The head related characteristics represented 57.1, 55.5 and 30% of the characteristics found as being different for D. sargus, D. puntazzo and L. mormyrus, respectively.
Fig. 2. Bivariate mean 95% confidence ellipsoids for the canonical discriminant-factor scores for white seabream (D. sargus): Portugal (*); Spain (D); Italy (^); Greece (&).
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J. Palma, J.P. Andrade / Fisheries Research 57 (2002) 1–8
Fig. 3. Bivariate mean 95% confidence ellipsoids for the canonical discriminant-factor scores for sharpsnout seabream (D. puntazzo): Portugal (*); Spain (D); Italy (^); Greece (&).
Fig. 4. Bivariate mean 95% confidence ellipsoids for the canonical discriminant-factor scores for striped seabream (L. mormyrus): Portugal (*); Spain (D); Italy (^); Greece (&).
J. Palma, J.P. Andrade / Fisheries Research 57 (2002) 1–8
Percent classification success (PCS) for each sample, the generalized Mahalanobis distances (D2) and F-statistics for the three species are presented in Table 3. There is also a reasonable degree of distinction between samples with a constant separation of the Portuguese sample from those of Spain, Italy and Greece, as shown by the mean values from the canonical analysis (Table 4). The Portuguese samples always correlated negatively with CV1, while no distinct pattern was found for the other samples. D. sargus (Fig. 2) and D. puntazzo (Fig. 3) presented a geographical gradient, which was not the case for L. mormyrus (Fig. 4).
4. Discussion Many nongenetic markers have often been used to understand the population structure and the movement of fish (Garcia de Leo´ n et al., 1997), though it is not easy to determine the correct spatial scale to employ in identifying populations of fish (Larkin, 1981). The presence of natural geographical barriers contribute largely to the emergence of differences between populations. A phylogeographic break due to the Straits of Gibraltar has been referred to by several authors (e.g. Borsa et al., 1997; Naciri et al., 1999), and we confirm that a difference exists between the Atlantic and Mediterranean populations for the three species studied. Although sharing the same habitats, D. sargus and D. puntazzo (Macpherson et al., 1997) have dissimilar morphological characteristics. Differences between populations of D. sargus are based on a smaller number of morphological characteristics than for those of D. puntazzo. In both cases, more than 50% of the characteristics are head-related, which suggests the influence of habitat differences between the populations. In fact, feeding is a well-known factor that influences head morphology (e.g. Gerking, 1994; Hyndes et al., 1997; Delariva and Agostinho, 2001). Thus, if different populations of a same species show discordant pattern of head morphology, this is often due to the exploitation of different ecological niches, specially constrained by the availability and type of prey. These differences are based on orographic and physical characteristics of each habitat (e.g. water temperature,
7
currents) induced by geographical dissimilarities. Similar results regarding the head morphology were obtained by Sara` et al. (1999) on cultivated D. puntazzo reared under different conditions. When genetically analysed (Bargelloni, unpublished data), these same samples produced similar results, with higher degree of homogeneity for D. sargus than for D. puntazzo and L. mormyrus. Furthermore, this study also found a genetic differentiation between the Atlantic and Mediterranean samples. A similar constraint, although with a lower significant differentiation, appeared between the East and eastern Mediterranean samples. Lenfant and Planes (1996) also refer to a genetic heterogeneity between samples of D. sargus from the Lion’s Gulf (France) and the Ligurian Sea (Italy). According to these authors, the break in the costal continuum and the complexity of local current systems, which may retard larval dispersal, could determine the degree of heterogeneity referred to above. Further studies on the ecological characteristics of this species could certainly assign the observed differentiation between populations. In the case of L. mormyrus, similar constraint for differentiation such as head morphology found for D. sargus and D. Puntazzo cannot be applied, since only 30% of the differentiating characteristics are head-related. Morphometric studies have been able to identify differences emphasizing them as helpful tools for the discrimination of fish populations. Truss analysis combined with image analysis is a step ahead to produce a better understanding of stock structuring of fish species. Acknowledgements This work was funded by the EC project AIR3CT94-1926. The work of Jorge Palma was funded by a Ph.D. Grant PRAXIS XXI/9441/96. The authors thank Dr. Marta Linde for her help during sampling of the Spanish sample. The manuscript benefited from the comments of two anonymous referees. References Bailey, K.M., 1997. Structural dynamics and ecology of flatfish populations. J. Sea Res. 37, 269–280.
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