CHAPTER - 2. Morphometric study of different stocks of Labeo fimbriatus using Truss analysis

Transcription

1 CHAPTER - 2 Morphometric study of different stocks of Labeo fimbriatus using Truss analysis

2 2.1. INTRODUCTION Identifying intraspecific units or stocks of a species with unique morphological characters enables a better management of these subunits of species and ensures perpetuations of the resources (Turan, 1999). Fish geneticists are interested to identify different stocks with regard to performance variation because identification of superior stocks in relation to performance of economically important characters is of paramount importance in forming base population for stock improvement programmes (Chetan, 2005). Phenotypically similar stocks (populations within species) when occur together in an area are less divergent and cannot be easily distinguished. Hence, it is necessary to identify characters that demarcate the stocks. The characters involved may be morphological, ecological, behavioral, or physiological; they are assumed to be genetically based (Pradeep, 2006). There are numerous methods to delineate the stocks and species based on morphological and genetic levels. The two popular methods of stock identification are i. Identification based on morphometries. ii. Identification based on gene frequencies. However, the major limitation of morphological characters at the intra-specific level, is that phenotypic variation is not directly under genetic control but subject to environmental modification (Clayton, 1981). Environmentally induced phenotypic variation, however, may have advantages in stock identification, especially when the time is insufficient for significant genetic differentiation to accumulate among populations. This phenotypic variation can be assessed by morphometric analysis. Morphometric analysis can thus be a first step in investigating the stock structure of species with large population sizes. 43

3 Since many years, morphometric investigations have been based on a set of traditional measurements which represent size and shape variation in the organisms. These measurements have been criticized as they are concentrated along the body axis with only sampling from depth and breadth, and most measurements are in the head. These conventional data sets are biased and they have got several weaknesses too. 1. They tend to be in one direction only (longitudinal). 2. Some landmarks are used repeatedly (tip of the snout and vertebral column). 3. Many measurements extended much over the body. 4. Amount of distortion (soft bodied animals) due to preservation cannot be easily estimated. 5. Many landmarks are external rather than anatomical and their placement may not be homologous from form to form. Thus, these traditional measurements represent a biased coverage of body form and success in selecting effective characters has been attributed to a matter of chance. As an alternative, a new system of morphometric measurements called the 'Truss network system' has been increasingly used for species and especially for stock differentiation. Truss has largely overcome the disadvantages of conventional morphometric studies. Truss analysis has been developed by fish taxonomists as a taxonomic tool to discriminate or quantify the difference between physically similar fish species and stocks (Strauss and Bookstein, 1982). Morphometric studies of this nature require the systematic measurement of distances (truss lines) between pairs of land marks across the body, thus forming a sequential series of connected polygons termed as truss box. The distances between the land marks provide more comprehensive coverage of form for greater discriminating power. It has been used in fisheries science for various purposes such as 44

4 1. Computing average body shapes and characterizing growth trends 2. Intergroup comparison 3. For stocky species identification 4. Estimate biomass of a population and to investigate shape characteristics 5. Quantification of ch anges in fish condition In the light of above facts, morphometric study of Labeo fimbriatus stocks of different river basins; the Cauvery, the Tungabhadra and the Vedavathi rivers of peninsular India was undertaken in the present study. As there is a very little knowledge on the morphometries of this fish species and study of this nature would be useful to understand the status of this species in natural water bodies. 45

5 2.2 REVIEW OF LITERATURE A popular definition of taxonomic species is: "Species are groups of interbreeding natural populations that are reproductively isolated from other such groups" (Mayr, 1963). Population of a species may not be confined to a single place but distributed over large area of different enviroiunents and such populations show little changes in their genotype or phenotypes leading the new races or stocks, etc. A fish stock can be defined as 'a local population adapted to a particular environment, having genetic differences fi'om other stocks as a consequence of this adaption' (MacLean and Evans, 1981). Morphological characters have been commonly used in fisheries biology to measure discreteness and relationships among various taxonomic categories. There are many well documented morphometric studies which provide evidence for stock discreteness. For example, morphological variation of European cyprinid, the chub, Leucius cephalus, within and across Central European drainages was successfully differentiated by morphometries (Jerry and Cairns, 1998). In any management regime, the identification of stock becomes a critical element. For the identification of these putative stocks at the practical level, the study of the population parameters and physiological, behavioral, morphometric, meristic, calcareous, biochemical and cytogenetic characters are usefiil. Of these, the morphometric investigations are based on a set of measurements of the body form (Hubbs and Lagler, 1947) and are of considerable importance for both taxonomic and management aspects as well. Morphometric study can be used to distinguish different stocks of a species. The study on the life history, morphology and electrophoretic characteristics of five allopathic stocks of lake white fish, Coregonus clupeaformis showed that morphometry could be used as a potential technique for discrimination of the stocks (Ihssen et al, 1981). 46

6 Morphological status of the Mesopotamian spiny eel, Mastacembelus mastacembelus populations from Karakay Reservoir, Tohma Stream and Tigris River were investigated using morphometric and meristic traits. Results revealed that there were significant morphometric differences among the populations. However, conventional morphometric data sets are biased and they have got several weaknesses too. To overcome these problems, a new method called the truss network was developed in which an even area coverage over the entire fish form was possible (Strauss and Bookstein, 1982; Rohlf, 1990; Bronte et al, 1999). Morphometric studies of this nature require systematic measurements of distances between pairs of landmarks across the body (truss lines and often called as 'trusses'), thus forming a sequential series of connected polygons termed as 'trussed' box (Strauss and Bookstein, 1982). Coverage of the trussed box must extend along the longitudinal, vertical and oblique axes for complete quantification of body shape. This method can discriminate stocks and species of varied fishes and prawns as well. Morphometry based on truss network data has been used for stock identification, species discrimination, ontogeny and functional morphology. Truss network analysis has been successfully used to discriminate and describe a wide array of morphologically-distinct species across a range of habitats. Such studies have involved commercially important species (Bronte et al, 1999), ecologically specialized species (Dynes et al, 1999), endangered species (McElroy and Douglas, 1995) and descriptions of new species (Rauchenberger, 1988). Winans (1984) found better results from the truss network study compared to morphometries while surveying differences among the three natural populations of Chinook salmon. Truss data provided more scientific information concerning shape changes among these populations. 47

7 The land mark based truss network analysis was carried out in six selected species of Serranid fishes. The results indicated the significant differences among species with respect to body height and caudal peduncle and these differences were related to differences in habitat and feeding habits among the species (Cavalcanti, 1999). The study on truss morphometric characterization of eight strains of Nile tilapia (Oreochromis niloticus) reported the significant differences between male and female sexes of the eight strains of Nile tilapia representing Egypt, Ghana, Kenya, Israel, Singapore, Taiwan and Thailand (Velasco et al, 1996). Effect of starvation on morphometric changes in the Chinese minnow, Rhyncocypris oxycephalus was studied by using truss analysis and the results indicated that the truss dimensions of the head and trunk region as well as the abdomen were increased significantly (P<0.05) through feeding or starvation. Truss dimensions of caudal region generally decreased through starvation particularly those dimensions at the hind part of the trunk. For over 30 years, most morphometric investigations based on the classical dimensions of length, depth and width of the fish shape, primarily in the head and tails have produced uneven and biased area coverage of the entire body. Hence truss study is of paramount significance discriminating shape variation offish (Parke/a/., 2001). The study on genetic heterozygosity and morphological variability among six species of freshwater sculpins, genus Cottus (Teleostei: Cottidae) revealed the existence of strong linear association between heterozygosity and morphological variance (Strauss, 1989). Garavello (1992) reported the geographical variation in Leporinus friderici (Bloch) from the Parana- Paraguay and Amazon river basins. The study on morphological variations among the populations of Leporinus friderici from three South American biogeographic regions: Parana- 48

8 Paraguay and Amazon river basins of Brazil and Marowizine river basin of Suriname using truss analysis revealed that the three populations overlap considerably in size for all morphometric characters, although mean values indicate the Suriname and Parana-Paraguay populations are similar and somewhat larger than the Amazonian population. Three stocks of Liza abu (mugilid species inhabiting Asia) from the rivers Orontes, Euphrates and Tigris were investigated using genetic and morphometric data. AUozyme electrophoresis for genetic comparison and the truss network system for morphometric comparison were simultaneously applied to the same sample set. Highly significant morphological differences were observed between the three stocks of Liza abu (Turan, 2004). To study the morphological differences between four populations of genus Coilia (Teleostei: Clupeiforms) and to identify them conveniently, Nine-teen point truss network was used. Results showed that populations of different Coilia species living in geographic proximity are more similar than conspecifics living farther apart and it concluded that separation and adaption are important to morphological difference (Cheng et al, 2005). The genetic and morphological variation of blue fish, Pomatomus saltatrix were studied based on morphometric and meristic analysis of samples collected throughout the Black Seas, Marmara, Aegean and eastern Mediterranean Seas. The study indicated existence of the three morphologically differentiated groups of Pomatomus saltatrix and the pattern of morphological differentiation also reflected their geographic isolation (Turan, 2006). Morphometric variation among sardine {Sardina pilchardus) populations from the northeastern Atlantic and the western Mediterranean was analyzed by truss analysis (Silva, 2003). The analysis explored the homogeneity of sardine shape within the area studied, as well as its relation to that of adjacent and distant populations (Azores and northwestern Mediterranean). 49

9 Principal component analysis on size-corrected truss variables and cluster analysis of mean fish shape using landmark data indicated that the shape of sardine off southern Iberia and Morocco is distinct from the shape of sardine in the rest of the area. The two groups of sardine are significantly separated by discriminant analysis, and their validity was confirmed by large percentages of correct classifications of test fish (87 and 86% of fish from the test sample were correctly classified into each group, respectively). Degree of differentiation among populations of twaite shad, Alosa fallax nilotica, in Turkish territorial waters was evaluated with the truss morphometric system (Turan, 2001) using Discriminant Function (DFA) and Principal Component Analysis (PCA). It revealed that the observed differences were mainly from posterior morphometric measvurements of the fish. The patterns of morphological differentiation suggested that there is limited exchange of individuals between areas to homogenize populations phenotypically fi-om the Black and Aegean seas to Eastern Mediterranean sea. Morphometric investigation was conducted on five species within the Labeoin group, of which four representing peninsular Malaysia while the fifth from Cambodia. The twenty seven characters chosen were measured on the truss network concept. The study showed the potential of these characters in clarifying less well defined species within this group (Siti Azizah et al, 2005). The morphometric and genetic analysis of Indian mackerel {Rastrelliger kanagurta) from peninsular India (Jayashankar et al, 2004) was undertaken in a holistic approach, combining one phenotypic (truss) and two genotypic methods (Protein polymorphisms and RAPD) to analyze possible population differences in Indian mackerel (Rastrelliger kanagurta) from selected 50

10 centers in the East and West coasts of India. The resuhs indicated no significant differences among the three populations of Rastrelliger kanagurta. Most of the studies indicated that the results of the morphometric anlaysis of different populations or species were corroborate with genetic anlaysis using RAPD markers (Jayashankar et al, 2004). However, cases where morphometric and genetic data indicate different scenarios of population structuring are also not uncommon (Salini et al, 2004; Levi et al, 2004). 51

11 2.3 MATERIALS AND METHODS Materials Source and details of the experimental animal The three stocks of Labeo fimbriatus representing Cauvery, Tungabhadra and Vedavathi rivers collected and maintained as described in were used for the present study. The details of experimental animal are given below. Scientific Name : Labeofimbriatus {QXoch., \191) Common Name Vernacular names Synonyms Rank : Fimbriatus, Fringe-lipped carp, 'Rohu' (of south India) : INDIA: Kemmenu (Kannada); Ven-candee, shall (Tamil); Ruchu (Telugu); Pudusi (Oriya); Tamthee, Tambra (Marathi) : Cirrhinusfimbriatus;Cyprinusfimbriatus;Labeo fimbriatus Rohita fimbriatus; Cirrhinus nancar; Cyprinus nancar, etc. : Species NCBI Taxonomy : Methods The three different stocks of Labeofimbriatusviz., Cauvery, Tungabhadra and Vedavathi formed the study materials. The fish harvested after the 24 weeks of growth trial period were sacrificed and kept in a deepfi-eezerat - 40 C before being used for the truss network study. A total of 90 specimens representing 30 numbersfi"omeach stock were used for the truss net work measurements (Table 2.1). The specimens consisted approximately of same age group of both the sexes. 52

12 Table 2.1 Details of flmbriatus stocks used for the morphometric study SI. No. 1 L.fimbriatus stocks Number Size range (g) Cauvery Tungabhadra Vedavathi Truss network analysis Pattern of size and shape variation of three different stocks of Labeo flmbriatus were evaluated by means of truss network analysis. A total of 90 specimens comprising Cauvery, Tungabhadra and Vedavathi stocks of flmbriatus (30 specimens from each stock) were used for the study Positioning The fresh specimens that were harvested and frozen after growth trials were used for the study. The frozen specimens were thawed and cleaned by keeping them under ruiming water. The water traces present were removed using blotting paper and dried gently. A drawing sheet was sandwiched between two thermacol sheets. The specimen was laid on thermacol sheet and body posture and fins were teased into a natural position in order to avoid error in measurements Pinning The morphological or anatomical land marks were selected along the outline of the specimen (Figure 2.1 and Table 2.2). A total often landmarks were identified on the specimens. Long round head pins were pierced at the 10 preselected landmarks in such a way that the tip of the pin left an imprint on the drawing sheet sandwiched between the thermacol sheets. The morphological or anatomical landmarks and position of pinning are shown in Plate

13 Networking and Measurement The pins and specimens were removed from the thermacol sheet. The drawing sheet was taken out and a series of connected quadrilaterals forming a truss network was drawn on the drawing sheet by joining the land marks with the help of micro tip pencil (0.5 mm lead). A 10 point truss network was constructed using standard morphological landmarks. With the use of an engineering divider, measurements were taken on 21 inter-landmark distances between 10 homologous landmarks (Table 2.3) using a standard truss network protocol (Strauss and Bookstein, 1982). All the measurements were taken in millimeter on a standard graduated scale with an accuracy of ±1.0 mm. Measurement was made on one side of the each specimen throughout the sampling (Plate 2.2 and Figure 2.2) Data analysis The truss network measurements made between anatomical landmarks were computed and the arithmetical comparisons of truss measures were subjected to multivariate techniques such as Factor Analysis (FA), Principal Component Analysis (PCA) and Cluster Analysis (CA) in Statistical Analysis Software (SAS, ver.lo). Factor analysis is a statistical method used to describe variability among observed variables in terms of a potentially lower number of unobserved variables called 'factors'. The purpose of factor analysis is to discover simple patterns in the pattern of relationships among the variables. In particular, it seeks to discover if the observed variables can be explained largely or entirely in terms of a much smaller number of variables called 'factors'. The variances extracted by the factors are called the 'eigen values. The factor with the largest eigenvalue has the most variance or grater discriminating power and so on. From the analyst's perspective, the factors with eigenvalues of 1.00 or higher are traditionally considered worth analyzing. One good rule of thumb for determining the number of 54

14 factors is the "eigenvalue greater than 1" criteria. Henry Kaiser (1960) suggested a rule for selecting a number of factors m less than the number needed for perfect reconstruction: set m equal to the number of eigenvalues greater than 1. This rule is often used in common factor analysis as well as in PCA. An alternative method called the scree test was suggested by Raymond B. Cattell (1966). In this method you plot the successive eigenvalues, and look for a spot in the plot where the plot abruptly levels out. Cattell named this test after the tapering "scree" or rockpile at the bottom of a landslide. PCA is a linear transformation that transforms the data to a new co-ordinate system such that the new set of variables, the principal components (PCs), is linear functions of the original variables. Similar to factor analysis, the first PC signifies most variance and so on. The last PC appears strident because they contain little variance. Further, confiision matrix using nearest neighbor with Mahalanobis squared distance fimction was used to calculate percent classification with respective errors in each stock to their respective origin. PROC CLUSTER of SAS (SAS'^'^ user's guide, 2000) was employed to generate clusters for graphical separation/demarcation offimbriatusgenotypes. 55

15 Fig. 2.1 Schematic ol Labeofimbriatusshowing the location of the 10 external landmarks for truss network analysis / ~-"--:5i Table 2.2 Anatomical landmarks selected for the study Land Particulars of mark Land mark 1 Tip of the snout 2 Upper end of the operculum 3 Origin of the dorsal fin base 4 End of the dorsal fin base 5 Upper origin of the caudal fin 6 Lower origin of the caudal fin 7 End of the anal fin base 8 Origin of the anal fin base 9 Origin of the pelvic fin base 10 Lower end of the operculum 56

16 Plate 2.1 Pinning at 10 different anatomical landmarks of Laheofimhriatiis Plate 2.2 Complete truss network as an overlay on image of Labeo fimbriatus Fig. 2.2 Reconstructed truss network for measurement between the land marks

17 Table 2.3 Truss distances between ten anatomical landmarks SI. Land mark Particulars of Truss distance No. Nos Tip of the snout - Upper end of the operculum Upper end of the operculum - Origin of the dorsal fin base Origin of the dorsal fin base - End of the dorsal fin base End of the dorsal fin base - Upper origin of the caudal fin Upper origin of the caudal fin - Lower origin of the caudal fin Lower origin of the caudal fin - End of the anal fin base End of the anal fin base - Origin of the anal fin base Origin of the anal fin base - Origin of the pelvic fin base Origin of the pelvic fin base - Lower end of the operculum Lower end of the operculum - Tip of the snout Upper end of the operculum - Lower end of the operculum Upper end of the operculum - Origin of the pelvic fin base Origin of the dorsal fin base - Lower end of the operculum Origin of the dorsal fin base - Origin of the pelvic fin base Origin of the dorsal fin base - Origin of the anal fin base End of the dorsal fin base - Origin of the pelvic fin base End of the dorsal fin base - Origin of the anal fin base End of the dorsal fin base - End of the anal fin base End of the dorsal fin base - Lower origin of the caudal fin Upper origin of the caudal fin - Origin of the anal fin base Lower origin of the caudal fin - Origin of the anal fin base 57

18 2.4 RESULTS Truss Network analysis A total of 90 specimens from all the three stocks (Cauvery Tungabhadra and Vedavathi) were kept in deep freezer and were used for the truss network analysis. The body weight of Labeo fimbriatus stocks used for truss analysis ranged from 31.05g to g and with an average weight of 61.31g. (Table 2.1). All the specimens were more or less of the same age group and were in good condition. The truss network measurements (21) made on three stocks of Labeo fimbriatus using different land marks (10) are presented in the Table 2.4. The data obtained from the truss network measurements was tested for normality and outliers were removed for the analysis viz., Factor analysis, and Principal Component Analysis (PCA) and Cluster analysis. The truss data was initially subjected to discriminate analysis by factor method using maximum multivariate statistical analysis as it was more effective in capturing information about the shape of an organism. Key characters used for the discrimination of the body form are those measures that have high eigenvalues. Results of the discriminant analysis indicated that the eigenvalue for the first factor was and the second was The eigenvalues of both the factors were found considerably low. These two factors combinely explained 52.0 % of total observed variation in size/shape characteristics in three L.fimbriatusstocks. Of these, the first factor explained 37%, the second factor 14% of variation (Table 2.5). The remaining factors contributed not more 10 percent variation to the total variance and the factor patterns revealed that these factors did not form any meaningfiil biologically explainable morphological groups (Kaiser, 1960). The proportion of variation explained by each factor (1 to 21) is depicted in the 58

19 Scree plot (Fig. 2.3). The plot drawn on the basis of factor analysis did not segregate the three fimbriatus stocks into separate groups which can be seen in Figure 2.4. The Principal Component Analysis (PCA) was employed for the multivariate description of morphometric data. In PCA, we had a sample of observations taken on a set of variables and the objective was to find linear combinations of variables, so that the first linear combination accounts for maximum possible variation in the data, the second linear combination accounts for next highest possible variation and so on. PCA combines and sunamarizes the variation associated with each of a number of measured variables into a smaller number of principal components (PCs) which are linear combinations of several variables that describe the variation in the shape in pooled sample. PCs were used to produce graphs to visualize relationships among the individuals of groups by plotting population centroids of first two principal components. In the present study, morphometric variation among three populations of L. fimbriatus was visualized via scatter plot of the scores of the first two principal component factors. This enabled the evaluation of the relation between the three stocks by means of proximity in the space defined by components. Results showed that the three stocks of fimbriatus did not form separate groups and is depicted in the Figure 2.5. Cluster Analysis (CA) involved the search through multivariate data for observations that are similar enough to each other to be usefully identified as part of common cluster. Cluster consists of observations that are close together and that the cluster themselves are separated. In the present study, the truss data was subjected to PROC CLUSTER procedures of SAS^"^. The samples of three fimbriatus population did not form any clusters and is depicted in the Figure 2.6. The cluster analysis also supports the un-group structwe highlighted by the Principal Components Analysis. 59

20 The different statistical components such as, Cluster Analysis, Principal Component Analysis (PCA), and Factor Analysis indicated no clear grouping of the three stocks (Figure 2.4, 2.5 and 2.6). Though, three stocks of Labeo Jimbriatus represented different geographical river systems, the present study indicated no variation exists among the three stocks of fimbriatus Percentage classification of difterent stocks of Labeofimbriatus The confusion matrix using nearest neighbor with Mahalanobis squared distance function of SAS {Statistical Analysis Software) was used to assess the percentage classification of each stock to its origin (Table 2.6 and 2.7). A total of 90 samples of three stocks were considered for the analysis and each stock represented by 30 samples. The results revealed that the Cauvery stock was represented 26.67% to its origin, where as 43.33% with Tungabhadra and rest 30.0% with Vedavathi. In the case of Tungabhadra 43.33% was representative to its origin, while 20.0% was indicated as Cauvery stock and remaining 36.67% was classified as Vedavathi stock. Vedavathi stock represented 46.67% to its origin, 20.00% and 33.33% were classified as Tungabhadra and Cauvery respectively. In addition, overall proportion for each stock to their respective origin was indicated with 33.33% accuracy. Percentage of individuals correctly classified to the three original populations is given in Table

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22 Table 2.5 Eigenvalues and proportion of variance contribution to total variance for truss distances (using Factor method: Maximum likelihood) Factors Eigenvalue Difference Proportion Cumulative ' Fig. 2.3 Scree plot of eigenvalues for truss measurement of Labeoflmbriatus stocks. 0.5 I " I''-'" I Variation of each factor O c O 0.2 E o Q. I 0., 0.0 nnrinnri r^^l Fl r-i r-i, Number of Factors

23 Table 2.6 The DISCRIM procedure for Nearest Neighbor with Mahalanobis Distances Observations 90 DF Total 89 Variables 21 DF Within Classes 87 Classes 3 DF Between Classes 2 Table 2.7 Confusion matrix with number of observations and percentage classification of each stock STOCK Cauvery Tungabhadra Vedavathi Total Cauvery Tungabhadra Vedavathi N % N % N % Total % Table 2.8 Class Level Information STOCK Frequency Weight Proportion Prior Probability Cauvery Tungabhadra Vedavathi

24 Factor? 4 Fig. 2.4 Factor analysis on three stocks oi Labeo fimbriatus Factor Analysis 2 : * DA * A ^ r I 1 1 I I r -2 O Factor-1 stock ^H'lc A A A t D D D v Fig. 2.5 Principal component analysis of three stocks o{ Labeo fimbriatus in2 PCa on the F imbi^ iatus data z: stock PrinI k t V 64

25 Fig. 2.6 Cluster analysis the three stocks of fimbriatus Distribution of Truss Observtion^true dusters Scatter* plot of observations!»fmrln2 4 S: o: HtA A D -i: ^ I I I 1 I I I I I [ I I I I I I I I I I 1 I I I I I I I I [ ' ' ' ' I 4 apr- in I stock >4=**k A A A t n n D v 65

26 2.5 DISCUSSION In general, fishes demonstrate greater variance in morphometric traits both within and between populations than other vertebrates, and are more susceptible to environmentally-induced morphological variation (Wimberger et al., 1992). The pattern of morphometric distinctness, detected within the species suggests a direct relationship between the extent of morphometric divergence and geographic isolation. Geographical isolation can result in the development of different morphological features between fish populations because of the interactive effects of environment, selection, and genetics on individual ontogenies produce morphometric differences within a species (PoUar, 2007). These distinctive species are known as races and referred as stocks in case offish species. One of the important considerations in the management of a fishery resource is the identification of discrete populations or stocks, which are generally defined as self maintaining groups, temporarily or spatially isolatedfi"omone another and considered genetically distinct or "a stock is an intraspecific group of randomly mating individuals with temporal and spatial integrity" (Ihssen et al, 1981; Booke, 1981). Failure to recognize or to account for stock complexity in management imits has led to an erosion of spawning components, resulting into a loss of genetic diversity and other unknown ecological consequences (Begg et al, 1999). Poor understanding of fish and fishery management can lead to dramatic changes in the biological attributes and productivity of a species. Rohlf (1990) has opined that phenotypic variation is more applicable to study short-term environmentally influenced differences betweenfishstocks. In view of the above, the present study was undertaken for the morphological discrimination of three different stocks of Labeo fimbriatus. 66

27 2.4.1 Truss network analysis The present study used multivariate statistical procedures viz., Factor analysis (FA), Principal Component Analysis (PCA) and Cluster Analysis (CA) to delineate three different populations of L. fimbriatus. Multivariate techniques simultaneously consider the variations in several characters and thereby assess the similarities among the samples. The key characters used for the discrimination of the body form are those measures that have high eigenvalues (Strauss and Bookstein, 1982). Truss network analysis is one such multivariate procedure used to demarcate phenotypically similar fish species and stocks. For example the study on morphological variations of five strains of Nile tilapia {Oreochromis niloticus) was identified by using truss net work analysis. The study succeeded in delineating the strains of 'America', Egypt' and the strains of '78', '88' as well as GIFT. Especially 'America' strain showed significant difference from the other strains of tilapia (Li Sifa et al, 1998). In the present study, truss measurements were made on three L fimbriatus populations using discriminant analysis by factor method. The first and second factors accounted for 37%, and 14% of the total variance respectively, together contributing to more than half of the total variance (52 %). From the analyst's perspective, the factors with eigenvalues of 1.00 or higher' is traditionally considered worth analyzing. Hence, the rest of the factors (3 to 21) were not considered during population differentiation. The scree plot suggested that only first two factors were meaningfiil and is considered for delineation of populations. Looking at the majority of the traits first factor could be termed as size factor, while the second factor as shape factor. The biologically meaningful grouping of first and second factor and the amount of variation they describe suggests the appropriateness of the factor analysis and importance to be attached to size and shape traits, for discriminating the stocks of I. fimbriatus. 67

28 A careful observation of the first factor suggests that majority of the trait loadings represent the anterior portion of the fish. However, middle measurement 4-8 was also loaded on factor 1. Looking at the majority of the traits the factor 1 can be generally termed as anterior portion (Silva, 2003). On the similar basis as above, the second factor is termed as caudal factor, in which the parameters that belong to caudal portion are loading. The biologically meaningful groupings of first and second factors and the amount of variation they are describing suggests the appropriateness of the factor analysis and importance need to be attached to anterior portion and caudal portion traits for discriminating the stocks of L. rohita. A large eigenvalues on Factor 1 which is loaded with anterior portion factors indicate that these parameters contribute substantially to the total variation and need to be incorporated in any stock discrimination analysis. The present study did not reveal any significant differences among the stocks. The truss network analysis clubbed all the three stocks into one group (Fig.6). This indicated that when the unit free observations were used (as factor analysis does) then there was no variation among the three populations of Z. fimbriatus. Similar tests were applied to construct a truss network on M mastacembelus specimens of Karakaya Reservoir, Tohma Stream and Tigris River. The discriminant function analysis for morphometric traits clearly separated three M. mastacembelus populations in this study (Cakmak and Alp, 2010). PoUar (2007) conducted a study on morphometric variability of Tor tambroides populations at Simanta, Nan Chong Fa and Wang Muang waterfalls, Khao Nan National Park in Thailand. The result of the multivariate analysis on 21 truss variables showed differences among 68

29 all the three waterfalls. From the discriminant analysis, the populations belonging to the three sampling sites were distinguished. Mathematical comparisons of truss measures use multivariate techniques such as Principal Component Analysis. The PCA is one alternative to FA (Factor Analysis), and is sometimes considered as more conservative and appropriate method for assessing morphological variation among groups. PCA is a data projection technique for summarizing variability in complex correlated data sets using a simple algorithm that finds major axes of variation in the data. In the present study. Principal Component Analysis (PCA) was used to interpret or explore the intraspecific variation by plotting the Principal Component scores (PC). The PCs which revealed significant differences were used for XY plots. Teisser was the first to interpret the first principal component of morphometric data as a multivariate index of size and secondary components as shape indices (Cadrin, 1999). A plot of first principal component was scored against the second principal component and the morphometric variation among three populations was visualized via PC scores on the scatter plot. The study revealed that the scores on the scatterplot did not show any clusters for the three populations but were scattered randomly on the plot. The results indicated that there was homogeneity in morphological structure among the three populations of Z. fimbriatus. Bagherian and Rahmani (2009) found the Principal Component Analysis an effective tool in the morphological discrimination of two populations of shemaya, Chalcalburnus chalcoides (Actinopterygii, Cyprinidae) in truss network analysis. Results showed that the populatibns and sexes were clearly separated by PCA. 69

30 In the present study, Cluster Analysis (CA) revealed that the three populations of L fimbriatus did not cluster but were spread randomly on the plot indicating no significant differences in their morphological characters among the three populations. Similar observation was also reported in few European cyprinids. Though high level of heterozygosity fits the general trend in European Cyprinids, this conclusion does not hold across fish families. For example, low levels of morphometric heterozygosity have been reported for the European perch Percafluvialities L, a common percid in all European waters (Heldstab, 1995). The study on genetic and morphological variation in a common European cyprinid, Leuciscus cephalus within and across Central European drainages of the river Rhine, Danube and Elbe using gel electrophoresis and morphometries indicated low level of divergence m. Leuciscus cephalus among sites and drainages (Hanfling et al, 1998). The factors suggested for the low level of divergence of Leuciscus cephalus in European rivers were 1. Recent divergence of populations. 2. Ongoing gene flow across drainages. Possible explanations for ongoing gene flow would be migration through artificial waterways, stocking by anglers or translocation by waterfowl. 3. High dispersal capacity also explains the low level of differentiation among the fish stocks. In the present study, all the three multivariate analysis (Factor analysis, Principal Component Analysis and Cluster analysis) provided qualitatively similar results. The three different population or stocks of L. fimbriatus were not clearly separated by the multivariate analysis. However, studies on morphology of fimbriatus in peninsular waters are limited and hence it would be difficult to authenticate the present findings. The study revealed insignificant 70

31 morphometric heterogeneity among three populations of L. fimbriatus representing Cauvery, Tungabhadra and Vedavathi rivers. The reason for homogeneity of these populations however, can be attributed to following factors 1. Recent divergence of the species. 2. River ranching programmes may have resulted in mixing of different populations. 3. Migration can also be the reason although it could be expected only between Tungabhadra and Vedavathi both of which are tributaries of River Krishna. 4. Tropical climate and more or less similar mode of habitat On the other part, environmental factors such as temperature, salinity, food availability or prolonged swimming may also determine the potential phenotypic discreteness. Separation and adoption are important for morphological difference between organisms. The potential capacity of populations to adapt and evolve as independent biological entities in different environmental conditions is restricted by the exchange of individuals between populations. A sufficient degree of isolation may result in notable phenotypic and genetic differentiation among fish populations within a species, which may be recognizable as a basis for separation and management of distinct populations (Turan, 2004). However, stock identification based on morphological characters must be confirmed by genetic evidence to verify that the phenotypic differences reflect some degree of reproductive isolation rather than simply environmental differences. On the other hand, stock discrimination by morphologic markers might be appropriate for fisheries management even if this phenotypic divergence is not reflected by genetic differentiation (Cadrin, 2000). 71

32 The classification of different stocks oi Labeoflmbriatususing nearest neighbourhood of Mahalanobis distance function indicated percentage contribution offish samples of each stock to their respective origin. All the three stocks were not represented to 100% accuracy in their origins but were represented less than 50% to their origin i.e. Cauvery stock with 26.67%, Tungabhadra with 43.33% and Vedavathi with 46.67%. However, all three stocks did not also classify completely (100%) according to their origin making it difficult to interpret and classify them into separate groups. The truss network analysis did not reveal any morphological differentiation among the three L. fimbriatus stocks. However, failure to detect the stock structure does not necessarily mean that there is no stock differentiation but probably is the failure of the method used for stock identification (Hay and McCarter, 1997). Such observations were made during the stock assessment of Herring in Baltic Sea, where previously no differentiation was detected. But later with the use of microsatellite markers significant structuring of herring was found in seven locations within Prince William Sound and Bering Sea (O'Connell et.al, 1998). 72

33 2.6 SUMMARY 1. Morphometric study of three stocks of Labeo fimbriatus was studied using 'Truss net work analyses', a systematic measurement of distances between pairs of landmarks across the body forming a sequential series of connected polygons termed as truss boxes. It covers entire fish body form and hence the complete quantification of body shape. This method is therefore, used in discriminating morphologically similar fish species and stocks. 2. The truss measurements (21) between 10 anatomical land marks were carried out on 90 specimens, comprised of Cauvery, Tungabhadra and Vedavathi stocks of L. fimbriatus (30 specimens per stock) and subjected to multivariate statistical analysis, viz., Factor Analysis (FA), Principal Component Analysis (PCA) and Cluster Analysis (CA). 3. Results showed that the three stocks of I. fimbriatus did not form separate group/clusters indicating no morphological variations between the three stocks. 73