CHAPTER - 2

Morphometric study of different stocks of Labeo fimbriatus using Truss analysis

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

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

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

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

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

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

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

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

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

2.3 MATERIALS AND METHODS

2.3.1 Materials

2.3.1.1 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 1.3.1 were used for the present study. The details

of experimental animal are given below.

Scientific Name : Labeo fimbriatus {QXoch., \191)

Common Name : Fimbriatus, Fringe-lipped carp, 'Rohu' (of south India)

Vernacular names : INDIA: Kemmenu (Kannada); Ven-candee, shall (Tamil);

Ruchu (Telugu); Pudusi (Oriya); Tamthee, Tambra (Marathi)

Synonyms : Cirrhinus fimbriatus; Cyprinus fimbriatus; Labeo fimbriatus

Rohita fimbriatus; Cirrhinus nancar; Cyprinus nancar, etc. Rank : Species

NCBI Taxonomy : 182801

2.3.2 Methods

The three different stocks of Labeo fimbriatus viz., 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 deep fi-eezer at - 40°C before being used for the truss network study. A

total of 90 specimens representing 30 numbers fi"om each 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

Table 2.1 Details of flmbriatus stocks used for the morphometric study

SI. No.

L.fimbriatus stocks Number Size range (g)

1

2

3

Cauvery

Tungabhadra

Vedavathi

30

30

30

25.64-105.59

31.05-95.19

37.15-87.03

2.3.2.1 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.

2.3.2.2 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.

2.3.2.3 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 2.1.

53

2.3.2.4. 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).

2.3.2.5 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

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 of fimbriatus genotypes.

55

Fig. 2.1 Schematic ol Labeo fimbriatus showing 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

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

Table 2.3 Truss distances between ten anatomical landmarks

SI. No.

Land mark Nos.

Particulars of Truss distance

1 1-2 Tip of the snout - Upper end of the operculum

2 2-3 Upper end of the operculum - Origin of the dorsal fin base

3 3-4 Origin of the dorsal fin base - End of the dorsal fin base

4 4-5 End of the dorsal fin base - Upper origin of the caudal fin

5 5-6 Upper origin of the caudal fin - Lower origin of the caudal fin

6 6-7 Lower origin of the caudal fin - End of the anal fin base

7 7-8 End of the anal fin base - Origin of the anal fin base

8 8-9 Origin of the anal fin base - Origin of the pelvic fin base

9 9-10 Origin of the pelvic fin base - Lower end of the operculum

10 10-1 Lower end of the operculum - Tip of the snout

11 2-10 Upper end of the operculum - Lower end of the operculum

12 2-9 Upper end of the operculum - Origin of the pelvic fin base

13 3-10 Origin of the dorsal fin base - Lower end of the operculum

14 3-9 Origin of the dorsal fin base - Origin of the pelvic fin base

15 3-8 Origin of the dorsal fin base - Origin of the anal fin base

16 4-9 End of the dorsal fin base - Origin of the pelvic fin base

17 4-8 End of the dorsal fin base - Origin of the anal fin base

18 4-7 End of the dorsal fin base - End of the anal fin base

19 4-6 End of the dorsal fin base - Lower origin of the caudal fin

20 5-8 Upper origin of the caudal fin - Origin of the anal fin base

21 6-8 Lower origin of the caudal fin - Origin of the anal fin base

57

2.4 RESULTS

2.4.1 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 105.59 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 0.00012661 and the second was 0.00004820. 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. fimbriatus stocks. 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

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

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.

2.4.1.1 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 2.8.

60

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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

1 0.00012661 0.00007841 0.3765 0.3765 2 0.00004820 0.00001462 0.1434 0.5199 3 0.00003358 0.00000572 0.0999 0.6198 4 0.00002787 0.00000904 0.0829 0.7026 5 0.00001883 0.00000377 0.0560 0.7587 6 0.00001506 0.00000130 0.0448 0.8034 7 0.00001375 0.00000219 0.0409 0.8443 8 0.00001156 0.00000282 0.0344 0.8787 9 0.00000875 0.00000249 0.0260 0.9048 10 0.00000626 0.00000095 0.0186 0.9234 11 0.00000531 0.00000068 0.0158 0.9391 12 0.00000463 0.00000032 0.0138 0.9529 13 0.00000431 0.00000132 0.0128 0.9657 14 0.00000299 0.00000038 0.0089 0.9746 15 0.00000261 0.00000078 0.0078 0.9824

16 0.00000183 0.00000023 0.0054 0.9878 17 0.00000160 0.00000053 0.0048 0.9926 18 0.00000107 0.00000039 0.0032 0.9958 19 0.00000067 0.00000027 0.0020 0.9978 20 0.00000040 0.00000005 0.0012 0.9989 21 0.00000035 ' 0.0011 1.0000

Fig. 2.3 Scree plot of eigenvalues for truss measurement of Labeoflmbriatus stocks.

0.5

I " O

c O 0.2

••E o Q.

I 0.,

0.0

I''-'" • I Variation of each factor

nnrinnri r l F l r-i r-i ,

10 15

Number of Factors

20 25

62

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 N

%

8 13 9 30 Cauvery N

% 26.67 43.33 30.00 100.00

Tungabhadra N

%

6 13 11 30 Tungabhadra N

% 20.00 43.33 36.67 100.00

Vedavathi N

%

10 6 14 30 Vedavathi N

% 33.33 20.00 46.67 100.00

Total

%

24 32 34 90 Total

% 26.67 35.56 37.78 100.00

Table 2.8 Class Level Information

STOCK Frequency Weight Proportion Prior Probability

Cauvery 30 30.0000 0.333333 0.333333

Tungabhadra 30 30.0000 0.333333 0.333333

Vedavathi 30 30.0000 0.333333 0.333333

63

Fig. 2.4 Factor analysis on three stocks oi Labeo fimbriatus

Factor Analysis F a c t o r ?

4

2 :

*

^—1—1—1—1—1—1—1—r

D A * A

—1—1—1—1—1—1—I—1—1—I—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—I—1—1—1—1—1—r

- 2 O

Factor-1

stock • ^ H ' l c A A A t D D D v

Fig. 2.5 Principal component analysis of three stocks o{ Labeo fimbriatus

PCa on the F imbi^ iatus data

in2

z:

stock

PrinI

+ + + k + + + t + + + V

64

Fig. 2.6 Cluster analysis the three stocks of fimbriatus

Distribution of Truss Observtion^true dusters Scatter* plot of observations

!»fMrln2 4

S:

o:

-i:

HtA

A D

^ — 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 — [ —

4 -2 0 2

apr- in I

s t o c k > 4 = * * k A A A t n n D v

' ' ' ' I

4

65

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 isolated fi"om one 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 between fish stocks.

In view of the above, the present study was undertaken for the morphological

discrimination of three different stocks of Labeo fimbriatus.

66

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

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

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

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

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

The classification of different stocks oi Labeo flmbriatus using 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

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