International Journal of Aquaculture, 2015, Vol.5, No.5 1

9
and Awad Elseed, 2014). The study also considered
the length – weight relationships and the type of
growth for each species. A clear description of each
species is given here after.
2 Material and Methods
2.1 Collection of fish
Specimens of
Hydrocynus
species were purchased
from the Central Market in Khartoum. Comparative
material included 30 specimens (10 from each o
(Castelnau, 1861),
Cuvier & Valencience,
1849) and
(Cuvier, 1819). Each individual
specimen was identified according to the original
description of Boulenger (1907). The institutional
abbreviations followed Daget and Grosse (1984).
2.2 Morphometric measurements
Twentyeight morphometric measurements and ten
meristic counts were taken for each specimen according
to Teugels and Thys (1990). All measurements were
taken on the right side of the specimens and were
pointtopoint measurements taken by a fine dial
caliper to (0.00) mm.
For descriptive purposes all
measurements were expressed as ratios of standard
length (%SL). The measurements of head structures
and interorbital width were expressed as percentage
of head length (%HL). Principal component analysis
(PCA) was used to explore the multivariate variable
data matrix to reduce the large number of variables
into a few biologically meaningful axes (principal
components) that explain as much variations as possible
(Past, 2005). Raw data of morphometric measurements
(not meristic counts) was transformed to logs10 and used
for multivariate analysis. Morphometric measurements
were log
10
 transformed to correct for length differences.
The loadings of the variables were done to determine
their importance on variability explained. Cluster
analysis of morphometric and meristic characters was
performed separately to identify the similarity of
individuals of each subspecies. Morphometric
measurements and meristic counts were analyzed
separately.The length–weight relationship and type of
growth of pooled data for each species were estimated
by the equation W =
a
L
b
, where W = weight of fish in
grams, L = SL in cm,
b
= length exponent (slope) and
a
= proportionality constant (intercept) according to
(Bagenal, 1978). The correlation or degree of
association (r
2
) between length and weight was calculated
from linear regression analysis.
3 Results
and Discussion
Twenty morphometric characters expressed as
percentage of standard length, % SL, and three
morphometric characters expressed as percentage of
head length, % HL, plus head length, were found to be
significantly different (P < 0.05) between the three
species (Table 1 & 2). The three species differ
significantly (p < 0.05) in the number of rays of
pectoral and anal fins, where
H. vittatus
has more fin
rays compared to the other two species (Table 3).
More scales of the lateral line; LLS; were recorded in
H. brevis
, and Dorsal– to– lateral line scales, DLS; gill
rakers, GR and teeth in the lower jaw, LJT are
significantly higher in
H. vittatus
.
Principal component analysis of data from the 27
morphometric measurement revealed that approximately
70.3% of the total variation was explained along one
component, (Table 4). The second component of
variation accounted for 11.5% of the total variability,
the third component of variation accounted for 4% of
the total variability and the fourth component of
variation accounted for 3% of the total variability. The
Eigenvalues for all components were positive
indicating that all used variables has some effect on
the morphological variation of the
Hydrocynus
species
The loadings of the morphometric variables to
determine their importance on variability explained is
presented in (Figure 1). Principal component analysis
of data from ten meristic counts revealed that
approximately 73.9% of the total variation was
explained along one component (Table 5). The second
component of variation accounted for 11.6% of the
total variability, the third component of variation
accounted for 4.5%, the forth component of variation
accounted for 3.3% and the fifth component of
variation accounted for 3.1% of the total variability.
The loadings of the meristic variables to determine
their importance on variability explained is presented
in (Figure 2).The data of log
10
–transformation of
morphometric measurements and cluster analysis of
meristic counts produced hierarchical clusters of
specimens of the three species in a distance dendrogram.
Most individuals of each species clustered together at
the end of the spectrum (Figure 3 & 4).
The lengthweight relationship was W =
3.088
L
2.079
2