MORPHOLOGICAL VARIATION AND LENGTH WEIGHT RELATIONSHIP OF Oreochromis mossambicus IN THREE

BRACKISH WATER SYSTEMS OF SOUTHERN SRI LANKA

H.M.T.N.B. Herath*, K.Radampola and S.S. HerathDepartment of Fisheries and Aquaculture, Faculty of Fisheries and Marine Science and Technology, University of

Ruhuna, Sri Lanka*Corresponding author:tharinduacademia@hotmail.com

Abstract

In the present study morphological variation in three Oreochromis mossambicus populations in southern Sri Lanka with special reference to the Nilwala estuary, Mawella lagoon and the Rekawa lagoon were studied. Twelve morphometric characteristics including Total length (TL), Standard length (SL), Body depth (BD), and Pre Orbital length (POL) etc. were analyzed using the one way ANOVA test and stepwise discriminant function analysis. One way ANOVA test results among morphological characters revealed that, characters regarding caudal fin length (CFL), Pre anal length (PAL), distance from anterior end of dorsal fin to posterior end of pelvic fin (ADPP), were significantly different among locations (p<0.05). Pre orbital length (POL) was significantly lower (6.10±0.96TL) in Mawella lagoon comparing with other two locations. Base length of anal fin (BLAF) was significantly highest (15.61±2.24TL) Rekawa lagoon population. Higher head depth (HD) was recorded in the Mawella lagoon fish population and it was significantly different from other two fish populations. These differences in morphometric characters were allowed to reject the null hypothesis that there was no morphological variation between the three Oreochromis mossambicus populations. In discriminant function analysis first function describes the 83.9% total group variance and second function describes 16.1% total group variance. Classification results revealed that 94.4% original groups were correctly classified into their original groups. These results indicate higher degree of population isolation among three groups. According to Length weight relationship for the three populations of fish was revealed that highest condition factor (2.17) recorded by the Nilwala estuary population and lowest condition factor (1.76) was in the Rekawa lagoon population. By using this condition factor data it can be concluded that Nilwala estuary population is much healthier than the other two fish populations.

Keywords: Brackish water, Discriminant function analysis, Length weight relationship, Morphometrics, Oreochromis mossambicus

Introduction

Oreochromis mossambicus was first introduced to the Sri Lankan fresh waters only in 1952 (Fernando & Indrasena, 1969). In Sri Lanka, this introduced Oreochromis.mossambicus has been most successful in stimulating the development of a major fishery and its continued sustenance (Silva, 1988). From its first introduction they have been subjected to the number of morphological variations all over the country. Underlined reasons for that kind of variations may be genetic variations or the geographic isolation. In addition to that hybridization with the lately introduced Oreochromis niloticus may be another governing factor for this variation.

Morphological plasticity according to environmental variability is commonly found among many fish species, predominantly in freshwater fish species. Phenotypic variation according to environmental

variability has been widely used by ichthyologists to differentiate among species and among populations within a species (Ihassen et al., 1983; Murta 2002). Morphometric is very important in biology because it allows quantitative descriptions of organisms. Quantitative approach allowed scientists to compare shapes of different organisms much better and they no longer had to rely on word descriptions that usually had the problem of being interpreted differently by each scientist (Gelsvartas, 2005). Analysis of phenotypic variation in morphometric characters or meristic counts is the method most commonly used to delineate stocks of fish (Cardin & Silva, 2005) and continues to have an important role in stock identification. Identification of morphologically discrete fish stock is much more helpful in deploying the management plans of desired species. Although there have been number of studies

conducted by different authors in different fish species, there have been no any research conducted over the morphological variation of Oreochromis mossambicus in Sri Lanka.

Length weight relationship of a fish is another important component in various aspects in the forms of fishes’ biology and healthiness. It is also a basic criteria in fisheries science in order to gain the knowledge about various indirect aspect such as environmental variability, conditions that best to

fishes growth etc. According to the Pauly (1983) length weight relationship can be used to predict the fish’s weight in order to the yield assessments of fish. Length- weight relationship is also holds considerable importance in fishery because it shows relevance to fish population dynamics and pattern of growth on fish stocks. In addition to the length weight relationship condition factor is another important parameter in order to determining the healthiness of species.

Materials and methods

Sample collection

The Present study was carried out during the 20th August 2013 to 23rd of October 2013. Three brackish water eco systems in the Southern province of Sri Lanka were selected as the sampling sites, comprising Mawella lagoon (M), Rekawa lagoon (R) and Nilwala estuary (N) (figure 1). From each location 30 specimens of matured Oreochromis

mossambicus were collected from the fishermen. After collecting, fish were transported using the ice chest to the Faculty of Fisheries and Marine Science and Technology for further analysis. Each fish specimen were drained off by using the filter paper, and subsequent identification number was given in order identify them

.

Figure 01: Oreochromis mossambicus collected locations

Morphometric measurements

All the measurements were taken from the left lateral side of fish. In each fish specimen 12 morphological distances were defined following the identified landmark distances (figure 2). Measurements were

done by using the digital venire caliper (Johansson digital meter) to the nearest 0.01mm, using the horizontal and vertical distances between identified landmark points.

Table 1: Summary of the morphometric measurements obtained for Oreochromis mossambicus

Morphometric measurement Abbreviation Description Distance

Total length TL Tip of the snout to the rear end of dorsal fin

1

Standard length SL Tip of the snout to mid-point of caudal fin

2

Body depth BD Maximum vertical distance of the body 3

Pre orbital length POL Tip of the snout to the anterior part of orbit

4

Orbital diameter OD Distance in between the anterior and posterior part of the orbit

5

Base length of anal fin BLAF Distance in between the origin of anal fin to the end of anal fin along its base

6

Caudal peduncle length CPL Distance in between posterior end of caudal fin to midpoint of caudal peduncle

7

Length of anterior end of

dorsal fin to posterior end of pelvic fin

ADPP Diagonal distance between anterior end of dorsal fin and posterior end of pelvic fin

8

Length of anterior end of

dorsal fin to posterior end of anal fin

ADPA Diagonal distance in between origin of dorsal fin to posterior end of anal fin

09

Pre anal length PAL Distance in between tip of the snout to the origin of anal fin

10

Head depth HD Vertical distance along the opercula margin in between the dorsal head margin and ventral head margin

11

Caudal fin length CFL Distance in between the midpoint of caudal fin to the posterior end of caudal fin

12

Figure 2: a Schematic diagram representing the morphometric measurement of Oreochromis mossambicus

Length weight relationship

Length-weight relationships were calculated using the equation W=aLb (Ricker, 1973). Relationship between length and weight was calculated by using the simple linear regression (Zar, 2010) method using the SPSS version 17 statistical package. W is the weight of fish (g), L is total length of fish (cm), b is regression coefficient between log W and log L. a is intercept of regression line. As a working formula, log W= log a+blogL was used. Condition factor was calculated by using the formula K=100W/L3 (Pauly, 1983).

Statistical analysis

Variation of the morphometric characters of fish should be attributable to body shape differences, and not related to the relative size of the fish (Mollah et al., 2012). To remove the correlation of morphometric character measurement with the body size, and standardization of data done by the equation ACi=log OCi-{β*(log TLi-log MTL)} (Claytor and Maccrimon, 1987). In here ACi= adjusted logarithmic character measurement for ith fish, OCi= observed character measurement for ith fish, β is the common within group regression co-efficient of that character and total length after both measurements were converted to logarithmic value. MTL= mean total length of fish, using all fish in all groups. After

application of the formula for each morphometric character, correlation analysis was done for each standardized morphometric character against total length of the fish in order to find out the removal of size dependence. Size standardized data were subjected to one way ANOVA, in finding out the differences in each morphological character between each localities. Test were considered under 5% significance level, followed by Turkey HSD post hoc test.

Stepwise Discriminant Function Analysis (DFA) were then performed to standardized characters in order to derive the classification functions which describes correct assignment of the individual with their a priori geographical location. Significance of the derived discriminant functions were determine by the chi square test and wilks lambda procedure. DFA also used to identify the most important characters that able to differentiate fish populations using F-value criterion. (F-entry, 3.84, F-remove-2.71). In here all the analysis were done by using the SPSS version 17 statistical package.

Results

Size statistics revealed that largest fish were recorded from the Nilwala estuary and lowest size fish were recorded from the Rekawa lagoon. The sex ratio of for all locations was male biased. Large number of

males were recorded from the Rekawa lagoon (n=20), meanwhile lowest number of males were recorded from the Nilwala estuary (n=13). Mean TL

for all fishes from three locations were recorded as the 17.65±2.88 cm (Table 2)

Table 2: Collection sites, Sample size and size ranges of adult Oreochromis mossambicus in samples.

Abbreviation Location n Sex ratio

( Male: Female)

TL/ cm Mean TL/ cm SD

M Mawella lagoon

30 1.5:1 13.60 - 19.50 17.01 1.42

R Rekawa lagoon

30 2:1 13.36 - 19.80 15.36 1.39

N Nilwala estuary

30 1.3:1 16.45- 24.78 20.22 2.02

Morphometric character variation between localities

Correlation analysis of the size standardized data with the total length of the fish, showed that observed, 12 characters successfully removed their dependence of size. According to the one way ANOVA test results, characters regarding CFL, PAL, ADPP, characters were significantly different among location. Difference in morphometric characters were allowed to reject the null hypothesis that there was no morphological variation between the three Oreochromis mossambicus populations. Significant

difference in mean standard length (SL) was found in Mawella lagoon fish population. Considering BD significant difference found in Mawella lagoon. POL was significantly different in Mawella lagoon comparing with other two locations. Base length of anal fin (BLAF) was significantly different between Mawella and Rekawa lagoon populations. Higher head depth (HD) was recorded in the Mawella lagoon fish population and it was significantly different from other two fish populations. But characters regarding OD, CPL, PAVC, and ADPA, significant differences were not found among locations. (Table 3)

Table 3: Summary of the Morphometric characters after the size standardization.

Mean (±SD) in different morphometric characters between the different sites as a percentage of mean TL for each group. (n=30)

Character abbreviation Mawella lagoon (M) Rekawa lagoon (R) Nilwala estuary (N)

SL 81.85±1.36b 80.22±1.20a 80.74±1.85a

BD 35.72±1.86a 37.10±1.85b 39.48±2.95b

POL 6.10±0.96b 7.23±1.20a 6.35±1.15a

OD 5.02±0.79 5.74±0.65 5.79±0.84

BLAF 13.88±1.41a 15.61±2.24b 13.37±1.13ab

CPL 12.13±0.52 12.44±0.82 12.12±0.58

PAVC 7.67±0.82 8.31±1.04 7.37±0.67

ADPP 38.69±1.63a 35.97±1.93b 36.85±1.92c

ADPA 53.60±1.67 52.03±1.92 51.18±1.70

PAL 59.02±1.88a 55.8±2.60b 58.33±2.04c

HD 36.70±1.64a 34.04±2.68b 33.85±3.11b

CFL 18.81±1.32a 20.87±1.29b 20.21±1.48c

In each row superscripts letter indicated one way ANOVA results for size adjusted characters. Measurements with different superscripts in each row are significantly different from each other, (p<0.05).

Discriminant function analysis of Morphometric characters after size standardization of data

According to the obtained results there were two functions that derived by the stepwise discriminant analysis. First function explained 83.9% of total variance in the observed morphological variation in data. Meanwhile second function explained 16% of

the observed variation in the data. Together with these two functions 100% variation were explained. (Table 4). According to the wilk’s lambda criterion first function had separated the cases into groups in much accurately over the second function by having smaller wilk’s lambda. Yet both functions are statistically significant in discriminating the cases into groups (p<0.05) (Table 5)

Table 4: Summary of the canonical discriminant functions

Function Eigen Value Variance (%) Cumulative Canonical correlation

1 6.333a 83.9 83.9 .929

2 1.211a 16.1 100 .740

First 2 canonical discriminant functions were used in the analysis

Table 5: statistical significance of the derived discriminant functions

Tests of functions Wilks Lambda Chi-square df Sig.

1 through 2 0.062 236.800 10 0.00

2 0.452 67.444 4 0.00

Obtained structure matrix revealed that first discriminant function heavily correlate upon the PAL, meanwhile second discriminant function revealed that it was positively correlated by the CFL and PAVC and negatively correlated by the BD. (Table 6)

According to the unstandardized canonical coefficients, discriminant function one is heavily depends upon the ADPA, BD, PAL and the PAVC, and CFL. Discriminant function two was depended upon the same characters. In order to predict the individuals group, scores obtained by the two functions were used. (Table 6)

DF1= (-17.362) + (-2.715×PAVC) + (1.064×PAL) + (0.784×BD) + (-0.731×ADPA)

+ (3.308×CFL)

DF2= (0.854) + (2.005×PAVC) + (-0.002×PAL) + (3.408×CFL) + (-0.1995×BD) +

(-0.266×ADPA)

Table 6: Unstandardized canonical coefficients

Function (DF)

1 2

ADPA -0.731 -0.266

BD 0.784 -0.1995

CFL 3.308 3.408

PAL 1.064 -0.002

PAVC -2.715 2.005

( Constant) -17.362 0.854

Unstandardized coefficients

Derived discriminant functions were correctly classify the individuals with their original groups with 94.4% classification success. In here Nilwala estuary population was classified with the highest

classification rate representing 100% classification success. Mawella and Rekawa lagoons’ populations were classified with a rate of classification success 93.3% and 90.0% respectively. (Table 7)

Table 7: Classification results based upon the derived Discriminant functions by stepwise discriminant function analysis for each group

Fish location Predicted group membership Total

M R N

M 28 2 0 30

R 3 27 0 30

N 0 0 0 30

% M 93.3 6.7 0 100.0

R 10 90 0 100.0

N 0 0 100.0 100.0

a. 94.4% of original grouped cases correctly classified.

Length weight relationship of the populations

Length weight relationship for the three population of fish was revealed that highest condition factor recorded by the Nilwala estuary population and lowest condition factor was in the Rekawa lagoon population. Higher value of b was recorded in the Nilwala river population and lowest by Mawella river population.

Value of, ‘a’ was smallest in the Mawella lagoon population and highest in the Nilwala river population. Nilwala river population fish, was largest among the three populations and smallest size fish were recorded from Rekawa lagoon. (Table 8)

Table 8: length weight relationship of Oreochromis mossambicus in different localities

Location Range of TL (cm) Range of W (g) Value of a Value of b R2 KM 13.60 - 19.50 54.67 - 177.5 1.4849 2.8386 0.7591 2.0880R 13.36 - 19.80 41.90 - 132.32 1.9117 3.1325 0.9134 1.7653

N 16.45- 24.78 83.26 – 306.23 1.9578 3.2359 0.9010 2.1778

1.12 1.14 1.16 1.18 1.2 1.22 1.24 1.26 1.28 1.30

0.5

1

1.5

2

2.5

f(x) = 2.83856801081176 x − 1.48494919347705R² = 0.759144590980225

LOG TL (cm)

LOG

W (g

)

Figure 03: length-weight relationship for the fish from Mawella lagoon

Figure 04: length weight relationship for the fish from Rekawa lagoon

1.1 1.15 1.2 1.25 1.3 1.350

0.5

1

1.5

2

2.5

f(x) = 3.13252738662493 x − 1.91167133218083R² = 0.913381237219545

LOG TL (cm)

LOG

W (g

)

1.2 1.25 1.3 1.35 1.4 1.450

0.5

1

1.5

2

2.5

3

f(x) = 3.2359440008713 x − 1.95775896818728R² = 0.901018784646765

LOG TL

LOG

W

Figure 05: length weight relationship for the fish from Nilwala estuary

Discussion

Length weight relationship

It has been reported by some fish biologists that ‘b’ values usually range from 2.5 to 4.0 for many fish species (Pervin and Mortuza, 2008). According to the observed length weight relationship of Oreochromis mossambicus, both Rekawa and Nilwala estuary populations show positive allometric growth since the b value they have gained exceeding the value 3 which were similar to the 3.1325 and 3.2359 respectively. But Mawella lagoon population has gained their b value which is similar to the 2.8386, that showed negative allometric growth. Negative allometric growth implies the fish becomes more slender as it increase in weight while positive allometric growth implies the fish becomes relatively stouter or deeper-bodied as it increases in length (Riedel et al., 2007). This was evident that Mawella lagoon population have relatively shorter body depth, (35.72±1.86 as % of TL) and Rekawa lagoon and Nilwala estuary populations have significantly larger body depth. (37.10±1.85 and 39.48±2.95 as % of TL respectively)

In considering with the condition factors, higher condition factors was revealed by the fish samples from the Nilwala estuary and lowest from the Rekawa lagoon. Changes in Condition factor can be occurred by various reasons. According to Khallaf et al., (2003) condition factor of fish can be affected by a number of factors such as stress, sex, season, availability of feeds, and other water quality parameters. Rekawa lagoon is mainly fed by the fresh

water stream named Kirama oya. Apart from the main freshwater inflow, there are

two small freshwater streams function only in rainy season and provide surface runoff from the suburb

(Priyadarshana, 1998). RSAMCC (1996) stated that limited fresh water which reaches the Rekawa lagoon through the three rivers which drain into the lagoon is mostly runoff from agricultural land. This water carries nutrients from fertilizer applications in rice fields, some pesticides and sediments deteriorating the water quality of the lagoon. Such kind of pollution may cause to the reduction of condition factor of Oreochromis mossambicus populations in the Rekawa lagoon. However this study was carried out in short time period and, large temporal variation of water quality parameters couldn’t be obtained. Zargar et al., (2012) showed that condition factor of Carassius carassius show strong correlations with environmental factors after long term study. In lagoons, they have geomorphic characteristic showing shallow depth, sluggish and slow flow dynamics, usually no large rivers flow into it. (Miththapala, 2013). But estuarine ecosystems are usually deeper, fast flow dynamics and always river flow occur. With relation to the production criteria, estuaries are more productive, than lagoons, due to its shallow depth that enhances the light penetration and increasing of primary productivity. This condition may helpful in enhancing the condition factor of fish in Nilwala estuary by gaining higher food abundance.

As a fishery management tool, length weight relationship provide the important information. Length weight relationship of a fish species can be used to estimate the bio mass of fish populations, production yield. Kimmerer et al., (2005) have used length weight data, for successful estimation of biomass of fish. Obtained results would be helpful in preliminarily for such kind of biomass assessment.

Analyzing of the morphometric data

From all three locations observed fish samples showed that higher male biased populations. High number of males (n=20), were recorded from the Rekawa lagoon and lowest from the Nilwala estuary location (n=13). Considering the length weight relationship of populations, it was revealed that higher condition factor (2.1778) was recorded from the Nilwala estuary population.

According to Kumolu & Ndimele (2010) condition factor reflects through its variation, information on physiological states of fish with relation to welfare. Le cren (1951) stated that it provide the information of gonad development of fish. Essentially, higher number of females (n=17) and higher condition factor in the Nilwala estuary population, might indicate that females of this population were reached to their gonadal maturity. On the other hand this was externally observed, by larger mean BD comparable with the other two populations.

During this study, no meristic measurements were taken to identify the variation between the populations. Study was carried out by Vidalis et al. (1994), stated that meristic characters of fish could be change in very narrow range, and change of meristic characters from acceptable range could be fatal to the individual.

Considering the morphological characters, significance variation was found between the CFL, ADPA and PAL in three locations. These results indicated that most of morphological variation of fish were found in both anterior part and posterior part of the body. Difference in those morphometric characters may be related to the environmental differences including the temperature, Salinity, hardness, alkalinity etc. or genetic differences that induced the morphological variability. Consider with the other morphological variants, higher OD (5.79±0.84) was found in Nilwala estuarine populations. Mattews (1988) stated that, diameter of fish eye may be caused by the turbidity among rivers. Dyer (1986) pointed out that higher concentrations of suspended sediment occur in the estuary than in either the river or the sea. High OD of Nilwala

estuary population might due to the turbidity variation in order to perfect visualization under the water. Long term studying the variation of turbidity, may be helpful in finding out the reason behind the higher OD.

Pre Orbital length (POL) was significantly different and shorter comparing with other two populations in the Mawella lagoon. Head depth of Mawella lagoon fish population were significantly different from the other two locations. Anal fin of a fish is primarily use for the stabilizing the locomotion of fish, in this context higher anal length may be gain great importance towards the Rekawa lagoon population.

With relation to the caudal fin of fish, significant variation could be found in the Mawella lagoon population. Caudal fin increase the locomotion and swimming ability of fish. According to the Gosline (1971) perfection of caudal locomotion has probably been the single greatest achievement of the teleostean fishes. Increasing length and width of the caudal fin may increase the surface area of fish and increase the maneuvering of the fish. In this prospect, Mawella lagoon population may show less burst in swimming than other two. However before come into conclusions, it will be helpful in find out hydrodynamics on fin locomotion in this localities. Since estuaries are high flow rate, the fin related characteristics may be helpful in maneuvering swimming against currents. This can be visualized by the increased and significantly higher PAL in the Nilwala estuary population.

Discriminant function analysis

Maric et al., (2004) stated that discriminant analysis is common method used to identify fish populations. According to obtained discriminant functions, three populations could be separated by using the PAL, CFL, BD, ADPA, and PAVC. In deriving the discriminant functions both first and second functions depend upon above characters. Samaradivakara et.al (2010), obtained same results for Oreochromis niloticus in different geographical regions for BD of fish. In general, fish demonstrate greater variances in morphological traits both within and between populations than other vertebrates, and are more susceptible to environmentally induced morphological variations (Allendrof et al., 1987). In this context, morphometrics related to the body of fish have utilized by the different authors. Present results indicated that the observed morphological variation in three Oreochromis mossambicus populations help in differentiate three populations. According to the canonical discriminant functions obtained, Nilwala estuary population could be

differentiated from other two with great variance. Such variation could be geographical variation, and adaptation to the inhabiting to distinct environment. Long term studying additional the physico chemical parameters may provide the reasons for such population variation. In addition to that it can be hypothesized such kind of population structuring may be occurred by the Parental protection of the younger ones of Oreochromis mossambicus in their life strategy. Since they are mouth brooders fries will remain their respective environment considerable higher time than to other species which influence on variation in the morphology of fish.

According to the Turan et.al (2010), stated that higher degree of morphological variation may lead to think that, populations may belong to the other taxa. However in observed populations, showed basically Oreochromis mossambicus characteristcis, and further study needed to differentiate such populations from hybrids.

De Silva & Hettiarchchi (2001), stated that tilapians in Sri Lankan reservoirs can show the identical morphology to one species, yet different maternal origin. For example, it was exemplified that a fish showing Oreochromis mossambicus phenotype could have the maternal origin of Oreochromis niloticus. Such kind of study needed the detailed genetic analysis and amalgamation of both genetic and statistical analysis may provide the better results.

On the other hand obtained discriminant functions were depend upon the fin related characters, including CFL, PAL. Such characters may be helpful in the field for proper identification of stocks from other two. In fisheries management view, identification of fish stocks have gained prominent importance before applying any management measures. In such a scenario observed characters to delineate the fish populations one another may be used as a fishery management tool. However further studying the additional morphological characters may be helpful in instant delineate the fish populations. It was evident that observed morphological variations are significant in some characters. Yet those characters change in very small amount in each locations. In this prospect DFA would be the best method in differentiating the populations with their a priori geographic locations.

Tilapians have introduced more than five decades ago in Sri Lanka, however with this shorter time duration it was revealed that morphological variations are existed in populations by various authors. This kind of morphological variation may be helpful in order to cope with the environment variation particularly changing climate of the world, Sea level rising that influence upon the brackish water environments of the world. However care should be given to frequent monitoring of such sensitive ecosystems in Sri Lanka, as undesirable variations may decline the brackish water fishery in Sri Lanka.

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