Microsatellite Analysis of Wild and Captive Populations of Asian Arowana (Scleropages formosus) in Peninsular Malaysia
Pek Yee Tang
The genetic variability of wild populations of green
arowana (Scleropages formosus) from Tasek Bera Lake and
Endau River and a captive stock of Malaysian golden arowana as an out-group
population was studied. The average number of alleles per locus was low,
ranging from 2.33 in the Endau River population to 2.56 in the Tasek Bera
Lake population. Departures from Hardy-Weinberg equilibrium were observed
in these three populations depending on the locus. Among nine loci, seven
significantly deviated from Hardy-Weinberg Equilibrium (p<0.05) in
the Tasek Bera Lake population whereas five significantly departed from
HWE (p<0.05) in the Endau River population. A deficit of heterozygosity
was observed from the analysis of these nine loci. This genetic data indicate
the wild population may be under stress due to inbreeding, over fishing
and deterioration in the breeding environment. The estimates of FST
and RST were statistically greater than zero for each comparison
and this suggested the existence of three genetically distinct populations.
However, the values of FST is low between Tasek Bera Lake and
Endau River populations, reflecting historical patterns of connections
between river systems in Peninsular Malaysia.
The river systems in Malaysia are rich in flora and fauna. Tasek
Bera Lake is a lowland alluvial riparian swamp system, in central Peninsular
Malaysia (Fig. 1). It lies within the catchments of Pahang
River, the Peninsula`s largest river. This wetland is nominated Malaysian`s
first RAMSAR site of November 1994. It is an excellent example of a blackwater
ecosystem, which includes open water, a reed swamp area and swamp forest
with grasslands on the periphery. The lake contains a large number (95
species of fish) of the country's freshwater fish species, including both
popular aquarium and sport fishes most of which are indigenous and include
the endangered Asian arowana, Scleropages formosus (Sim, 2002).
The Endau-Rompin area is situated at the boundary of Johor and Pahang
States in Peninsular Malaysia (Fig. 1). It is one of
the few remaining lowland forests in Peninsular Malaysia. It encompasses
the watershed of the Endau and Rompin rivers. The Endau River, flows across
the park in an easterly direction and drains into the South China Sea.
Water flow is more or less smooth and the water is less transparent than
in streams. A total 108 species in 26 families have been reported to be
present in the Endau basin (Ng and Tan, 1999). It was also reported that
the upstream portion of Endau River is the breeding ground of the arowana
The Asian arowana (Scleropages formosus) is also known as dragonfish,
Asian bonytongue, Kelisa or Baju rantai. This ancient Ossteoglossid
fish is one of the most expensive and sought after fish in aquatic world.
The Asian arowana, has been reported in Cambodia, Thailand, Malaysia,
Borneo and Sumatra (Pouyaud et al., 2003). There are three main
colour varieties (green, gold and red). The green and gold varieties are
found in Peninsular Malaysia (Tang et al., 2004). The green variety
is still relatively common in some areas such as Tasek Bera Lake in Pahang,
Endau River in Johor (Ng and Tan, 1999; Sim, 2002) and Trengganu drainage
area (Cramphorn, 1983). It is believed that the wild populations of the
golden variety are almost extirpated due to over
||Major river drainages and major habitats of arowana
(Scleropages formosus) in Peninsular Malaysia
harvesting and habitat modification. The arowana has been listed as one
of most highly endangered fish by CITES the Convention on International
Trade in Endangered Species of Wild Fauna and Flora (Greenwood et al.,
1996). Arowana is threatened with extinction and placed on the Red List
(Hilton-Taylor, 2000) of the International Union for Conservation of Nature
and Natural Resources (IUCN). Only the F2 generation from the
commercially captive-bred individuals can legally be exported. The survivability
of the arowana is further threatened due to some of its natural behavior
of low fecundity rate, oral-brooding habit and open-water spawning (Dawes,
To conserve such a declining species, it is necessary to have sound knowledge
on its biology, ecology, biogeography and inter-population genetic diversity.
From the genetic perspective, the aim of natural fishery management should
be to conserve intra-specific genetic diversity.
DNA fingerprints of arowana have been studied by different methods to
determine which methods were most suitable in providing information on
genetic variability. As the DNA fingerprint is a pattern made up of DNA
fragments that are resolved by electrophoresis, each individual has its
own unique fingerprint due to its genetic make-up. The capability of Restriction
Fragment Length Polymorphism (RFLP) and random amplified polymorphic DNA
(RAPD) techniques to asses genetic variability of arowana is low (Fernando,
1997). Analysis using Amplified Fragment Length Polymorphism (AFLP) have
shown significant genetic differentiation between the green, red and gold
arowana (Yue et al., 2002, 2004).
The use of microsatellites is well documented in the study of fish genetics
(Sekino et al., 2002; Was and Wenne, 2002; Elliott and Reily, 2003).
It is a powerful technique to detect genetic variability and can be used
to assess variability between and within captive strains of arowana (Yue
et al., 2000, 2004). However, genetic variation in wild population
of arowana has yet to be studied. In this study, we used microsatellite
markers to assess genetic variability of wild populations of green arowana
in the Tasek Bera Lake and Endau River in comparison with a captive population
of golden arowana as an out-group. Work such as this will considerably
help to protect the wild populations of arowana as the microsatellite
DNA fingerprint provides undisputed and reproducible evidence of relatedness
and population identification. This information can be applied for long-term
management of wild population as well as captive population through analysis
of their pedigree and heterozygosity and provide guidelines for captive
MATERIALS AND METHODS
Fish samples collection: A total of 135 individuals were obtained
from both of the habitats Tasek Bera Lake and Endau River. The locations
selection was based on where the aboriginal people catch arowana fishes.
Ninety-seven green arowana were collected from Tasek Bera Lake, Pahang
and thirty-eight green arowana were sampled from Endau River. Fishermen
used dugout canoes to scoop individuals after dark. The fish were located
from the reflection of theirs eyes in torchlight. Initially all the collections
were kept in polystyrene basins for several days. When the catch reached
10 individuals, the fish were shifted to the Ecology and Biology Laboratory,
Faculty of Science, University of Malaya and where it`s reared individually
in aquarium. Twenty-three Malaysian golden arowana samples were collected
from the Malacca Fisheries Department as an out-group population for comparison.
Tissue sample collection: Scales, over turned gill covers, fin
clips and muscle tissues sample were used for DNA extraction. Scales were
either removed from live specimens or were from accidental loss while
over turned gill cover and fin clips were only from live specimen. Muscles
were collected only from dead. These were preserved in either a laboratory
standard bottle or a 1.5 mL micro-centrifuge tubes containing 90% ethanol
until genomic DNA extraction was performed. All samples were stored at
DNA extraction and PCR: The method of DNA extraction from the
preserved tissues was modified from Asahida et al. (1996). Approximately
20-30 mg of specimen tissue was used per extraction in 260 μL TNES-Urea
buffer (6 M Urea; 10 mM Tris-HCl, pH 7.5; 125 mM NaCl; 10 mM EDTA; 1%
SDS) and 0.2 mg (muscle) to 0.8 mg (scale, fin and overturned gill cover)
of Proteinase K was added to the samples. The mixture was incubated for
12 h (muscle tissue) or 15 h (scale, fin and overturned gill cover) at
55 °C followed by conventional phenol-chloroform extraction.
Genetic variation within and between these populations was assessed using
nine microsatellite loci. Among these markers four were described by Yue
et al. (2000), while five loci were isolated by Tang et al.
(2004). The Polymerase Chain Reaction (PCR) procedure was performed on
a Hybaid Omnigene thermal cycler in total a volume of 25 μL. Reactions
contained 1x PCR buffer (Promega), 1.5 mM MgCl2, 200 μM
of each dNTP, 0.2 μM of each primer, 1U Taq polymerase (Promega)
and 20 ng of genomic DNA. Amplifications for the four loci described by
Yue et al. (2000) were carried out using 4 min of initial denaturation
followed by 30 cycles of 30 sec of denaturation at 94.5 °C, 30 sec annealing
at the temperature detailed in Table 1 and 30 sec extensions
at 72 °C, with a final extension of 5 min at 72 °C. The other five loci
were amplified with 3 min of initial denaturation followed by 40 cycles
of 10 sec of denaturation at 95 °C, annealing at specific temperature
(Table 1) for 10 and 30 sec extensions at 72 °C, with
a final extension of 5 min at 72 °C. Polymerase Chain Reaction (PCR) products
were run on a 10% nondenaturing polyacrylamide gel (16H20 cm) at
250 V for 4-5 h. A 20 bp DNA marker (Cambrex BioScience) was used to estimate
the PCR fragment size. Gels were visualized using DNA silver staining
system (Promega) and analyzed using GelCompar the II Software (Applied
Statistical analysis: Microsatellite allele frequencies of each
population at each locus were estimated using GENEPOP version 3.1c (Raymond
and Rousset, 1995). The observed (Ho) and expected heterozygosities
(He), of the polymorphic loci of each strain were estimated
using ARLEQUIN version 2.000 (Schneider et al., 2001). Hardy-Weinberg
equilibrium (HWE) at each locus was assessed using ARLEQUIN. A Markov-chain
method with 1000 steps, 1000 dememorizations was used to calculate an
unbiased estimate of the p-value. The inbreeding coefficient, FIS
(Weir and Cockerham, 1984) was estimated using the GENEPOP programme to
measure the HWE departures for each population. The software MICROCHECKER
(Van Oosterhout et al., 2004) using the
||Primers sequences of 9 microsatellite loci
Brookfield`s (1996) method was used to identify the presence of null
alleles. MICROCHECKER was also used to test for another source of errors
in mis-scoring due to stuttering and allelic dropout. Population differentiation
was measured by calculating pairwise weighted FST (Weir and
Cockerham, 1984) values over seven loci (D92, D27, D85, K10, K13, K16
and K27) using ARLEQUIN. RST values were also calculated as
sum of squared size differences based on number of repeats (Slatkin, 1995).
The probability associated with FST and RST values
was estimated through random permutation procedure (1000 permutation).
Microsatellite variability: All nine microsatellite loci were
polymorphic and exhibited variability in the three populations. Allele
frequency distributions for these populations are listed in Table
2. A total of 40 alleles were detected in 158 individuals. The average
number of alleles per locus ranged from 2.33 in the Endau River population
to 2.78 in the Malaysian golden population. Alleles D92116,
D2776 and K13206 were found in the Tasek Bera Lake
population but undetected in the Endau River population. Allele K13202
was only detected in the Endau River and the Malaysian golden population.
Fifteen alleles were only present in the Malaysian golden population while
15 alleles were only present in the Tasek Bera Lake and the Endau River
populations. Most of the alleles found in this study showed high frequencies
and rare alleles were not detected.
Hardy-Weinberg equilibrium: The Malaysian golden population showed
the highest average observed heterozygosity (0.522), followed by the Tasek
Bera Lake (0.301) and the Endau River (0.304) populations. The average
expected heterozygosity at the nine loci was highest (0.407) in the Malaysian
golden population, followed by the Endau River population (0.405) and
the Tasek Bera Lake (0.368) population. Positive values of inbreeding
coefficient (FIS) were observed for most of the loci across
all the 3 populations. The highest average FIS value (0.311)
was observed in the Endau River population, whereas the lowest (-0.256)
was seen in the Malaysian golden population. The data implicate inbreeding
in the Tasek Bera Lake and the Endau River populations (Table
Among the nine loci screened in the Tasek Bera Lake population, seven
deviated significantly (p<0.05) from Hardy-Weinberg equilibrium. Five
out of nine loci from the Endau River-populations departed significantly
from HWE (p<0.05). Five loci in the Malaysian golden population were
deviated (p<0.05) from HWE (Table 3). MICROCHECKER
indicated that departure from HWE in these three populations was not due
to stuttering or large allele drop out. Departure from HWE at loci D11
and K37 in the Tasek Bera Lake and Endau River populations was contributed
by null alleles. The frequencies of null alleles were 0.33 for D11, 0.17
for K37 in the Tasik Bera Lake population and 0.32 for D11, 0.09 for K37
in the Endau River population.
Genetic differentiation: Pairwise FST values indicate
that there is significant genetic differentiation (p<0.05) between
these three populations (Table 4). The Tasek Bera Lake
and the Endau River populations were the most similar. The population
differentiations based on FST and RST values were
similar but RST values were higher than FST values.
||Allele distributions for nine microsatellite loci in
three populations of arowana
Number of alleles
(N), observed heterozygosity (Ho), expected heterozygosity
(He) and number of studied specimens (n) at nine microsatellite
loci for three populations of arowana
*: Indicates a significant
deviation from Hardy-Weinberg equilibrium, p<0.05
||Pairwise comparisons of microsatellite
FST (below diagonal) and RST (above diagonal)
between 3 populations of arowana
|*: Indicates a significant genetic distance, p<0.05)
Deviation from Hardy-Weinberg equilibrium: In this study, most
of the loci screened deviated from HWE. The observed heterozygosities
observed in our study were lower than estimates by Yue et al. (2000,
2004) but comparable to estimates by Sivananthan (2004) and Tang (2004).
This may be attributed to the territorial behaviour of arowana in nature
(Scott and Fuller, 1976) where the entire population may be divided into
several subpopulations and mating between related individuals is more
common. Thus, inbreeding increases the proportion of homozygous individuals
in a population. Positive values of inbreeding coefficient (FIS)
further supported possibility of inbreeding in the Tasek Bera Lake and
the Endau River Populations. The presence of positive values of inbreeding
coefficient (FIS) could also be caused by the presence of null
alleles which has also been reported in a previous study of arowana (Tang,
2004). Bentzen et al. (1996) suggested null alleles might be responsible
for significant deficiency of heterozygotes. An alternative explanation
is that although the individuals were sampled as far as possible, they
may not represent the true population due to the natural behavior of the
arowana. The arowana is territorial in nature and fry could be collected
in the mouth of the males or are in the vicinity of adults. Thus, individuals
collected at the same location might often be siblings produced by a relatively
Microsatellite variability: The distribution of alleles per locus
in this study is less than Yue et al. (2000, 2002, 2004) but there
is considerable overlap in the distribution. The present result showed
that there was a lack of private alleles to differentiate the two green
arowana populations. Three private alleles were detected, 2 in the Tasik
Bera Lake population and 1 in the Endau River population. Individuals
which carried this allele might not be sampled there or there might not
be such an allele. Low sample sizes can impact the precision of estimates
of population structure and genetic distance. However, the sample size
of the Tasek Bera Lake population fell within the range recommended by
O'Connel and Wright (1997) and was higher than the sample size used by
Yue et al. (2000, 2002, 2004) but this population still displayed
a very low level of genetic variability. Thus, suboptimal sample size
should not be the main reason for the over all low genetic variability
in this study. However, sample size of the Endau River population was
lower than that recommended. Furthermore, this comparison is considered
biased because in the present study all of the samples were collected
from single region while the green arowana stock studied by Yue et
al. (2000) was pooled from farms in Singapore and Indonesia. The study
carried out by Yue et al. (2004) was based on stock established
from wild-caught arowana in the early 1980s and this might not represent
the recent natural populations. Low number of allele was also recorded
in a green arowana stock with unknown pedigree obtained from the Malacca
Fisheries Department (Sivananthan, 2004; Tang, 2004).
The numbers of alleles were found to be lower than those reported by
other authors in other fish species (Naish and Skibinski, 1998; McConnell
et al., 2001). Loss of allelic variation also has been reported
for Danish and Polish populations of brown trout (Hansen et al.,
2000; Was and Wenne, 2002). Tessier et al. (1997) observed significant
changes in allele frequencies and losses of low-frequency alleles, but
no reduction in heterozygosity of wild Atlantic salmon populations. Reduction
in allelic variation in the present study of arowana in wild populations
may be explained by the founder effect and genetic drift (Alam and Islam,
2005). It may thus be assumed that the populations might have a small
effective number of parents (Ne) as illegal fishing is an ongoing activity
at Tasek Bera Lake and Endau River even though the arowana is protected
under the State Fisheries Enactment, 1991. The estimates of effective
population size were very low for a natural populations (Rahman, 2007)
compared with captive populations (Yue et al., 2004). Thus, the
losses of alleles and heterozygosity may increase with bottlenecking and
inbreeding in the populations and it can be postulated that the wild population
may be under stress due to over fishing and deterioration in the breeding
environment. Populations that have recently undergone bottlenecks are
likely to lose rare alleles severely but may contain substantial amounts
of heterozygosity (Nei et al., 1975; Allendorf, 1986). Our results
showed that no rare allele was detected in these three populations.
A low fecundity rate further threatens the population size of the arowana.
Reduction in genetic diversity had been linked to decreases in growth
and fecundity, changes in sex ratio and the ability to adapt to environmental
changes (Chapman et al., 1999). Maintenance of genetic variation
is essential for long-term survival of populations. However, stock transfer
will be a difficult task. To date, no survey has been carried out to determine
the genetic diversity of the wild populations in other river systems such
as the Kahang River (Endau Drainage; Fig. 1) and Kenyir
Lake (Terengganu Drainage; Fig. 1). Furthermore, local
adaptation will be lost if the population is mixed with others (Haig,
1998). Another source of diversity will be the populations in captivity.
In captivity it is possible to develop this kind of diversified stocks
by selection and breeding and this had been reported for captive populations
of arowana by Yue et al. (2000, 2002, 2004). Through genetically
compatible introductions, some individuals from the hatcheries can be
released to their natural habitat to improve the gene pool (Philipp et
al., 1993). However since, the green arowana fetches the lowest price
compared to the gold and red varieties, its breeding programme in the
private farms is not extensive. Since the captive breeding involves selection,
it must be noted that the adverse genetic effects of stock transfer such
as outbreeding depression (Templeton et al., 1986; Philipp, 1991)
should be taken into consideration.
Genetic differentiation: Our estimates of FST and RST
were statistically greater than zero for each comparison and this suggested
the existence of three genetically distinct populations. However, the
value of FST is low between Tasek Bera Lake and Endau River.
The pairwise genetic differentiation provide evidence of underlying geographic
and temporal components of population divisions between these three populations.
The Tasek Bera Lake and the Endau River populations were collected from
two geographically distant drainages but FST suggest that gene
flow is high. The effect of geographical distance on FST and
gene flow (Nm) values has been reported in stream-living brown trout collected
from different sections of the main stream (Carlsson et al., 1999)
and Indian major carp Catla catla collected from three major rivers
(Alam and Islam, 2005). However, in this study, these two wild populations
showed a close relationship reflecting historical patterns of connections
between river systems in Southeast Asia (Voris, 2000). Heist et al.
(1996) attributed that relative genetic homogeneity between populations
of different regions does not always signify that separate fishery stocks
do not exist; reasons for this are historical associations and minimal
gene flow. The populations which may not have been isolated long enough
to established differences in allele frequencies, or exchange of individuals
between populations, even small population size is sufficient to maintain
the same alleles in different populations and contribute to allele genetic
similarity between in the same alleles in different populations.
Information on the genetic structure of natural fish species is
essential for optimizing fisheries management and stock improvement programmes.
The study revealed a relatively low level of genetic variation at microsatellite
loci within and between arowana populations, with genetic variation in
the natural population lower than the captive populations. Knowledge of
genetic structure of the wild as well as captive arowana populations is
indispensable for management of the populations in order to maintain their
This study was supported by short-term research grants 0139/2002A,
0142/2003A and 0131/2004A from the University of Malaya and the research
was partly funded by a research grant from the Ministry of Science, Technology
and Innovation, Malaysia (05-01-03-SF0172). We wish to express our sincere
thank to the Malaysian Government for granting the sponsorship through
the Malaysian Technical Cooperation Programme (MTCP) for the study.
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