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. Herath
Department 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
ADPP
Diagonal distance between anterior end
of dorsal fin and posterior end of pelvic
fin
8
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
dorsal fin to posterior end of pelvic
fin
Length of anterior end of
dorsal fin to posterior end of anal
fin
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
TL/ cm
Mean TL/ cm
SD
( Male: Female)
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
character
variation
Morphometric
localities
between
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
93.3
6.7
0
100.0
R
10
90
0
100.0
N
0
0
100.0
100.0
%M
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
M
R
N
Range of TL (cm)
13.60 - 19.50
13.36 - 19.80
16.45- 24.78
Range of W (g)
54.67 - 177.5
41.90 - 132.32
83.26 – 306.23
Value of a
1.4849
1.9117
1.9578
Value of b
2.8386
3.1325
3.2359
R2
0.7591
0.9134
0.9010
K
2.0880
1.7653
2.1778
2.5
LOG W (g)
2
y = 2.8386x - 1.4849
r² = 0.7591
1.5
1
0.5
0
1.1
1.15
1.2
1.25
1.3
LOG TL (cm)
Figure 03: length-weight relationship for the fish from Mawella lagoon
2.5
LOG W (g)
2
1.5
y = 3.1325x - 1.9117
r² = 0.9134
1
0.5
0
1.1
1.15
1.2
LOG TL (cm)
1.25
Figure 04: length weight relationship for the fish from Rekawa lagoon
1.3
1.35
3
2.5
y = 3.2359x - 1.9578
R² = 0.901
LOG W
2
1.5
1
0.5
0
1.2
1.25
1.3
1.35
1.4
1.45
LOG TL
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 deeperbodied 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.
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2.
3.
4.
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Allendrof, F. W., Ryman, N. & Utter, F.,
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wiely.
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
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Tilapians have introduced more than five decades ago
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