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All issues Volume 32 (2019) Aquat. Living Resour., 32 (2019) 4 Full HTML
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Aquat. Living Resour.
Volume 32, 2019
Article Number 4
Number of page(s) 11
DOI https://doi.org/10.1051/alr/2019002
Published online 15 February 2019

© EDP Sciences 2019

1 Introduction

Globally, freshwater resources are the most species-richecosystems with 40% of the total fish diversity. Ironically, they are one of the most threatened ecosystems as 20% of the fishes have been assessed under threatened category (Jenkins, 2003; Vörösmarty et al., 2010; Darwall and Freyhof, 2016). Various anthropological factors such as altering river corridors, introduction of alien species, pollution, dam construction and overfishing are responsible for ecosystem degradation (Dudgeon et al., 2006). Previously, species specific management measures were implemented to conserve/manage ecosystems. In recent past, the ecosystem-based approach, which considers each species/population with equal importance in ecosystem functioning, has been adapted by different nations to protect the aquatic resources (Trochta et al., 2018).

Lotic ecosystems are dynamic with respect to biotic and abiotic factors, accordingly its component species respond differently to these factors. It could result in heterogeneity in species/population genetic composition and loss of genetic integrity between populations/sub-populations. These isolated populations/sub-populations could accumulate changes in allele frequency and form as distinct populations (Laikre et al., 2005). Delineation and characterization of genetic structure are essential as each population would respond differently to the biotic/abiotic factors. As an integral part of ecosystem, populations of species have to be genetically characterized for formulating population specific management measures. Accordingly, FAO's Commission on Genetic Resources for Food and Agriculture and Convention of Biodiversity has undertaken the documentation of genetic structure of populations of several aquatic species for sustainable management (FAO, 2015).

India is one of the biodiversity hotspots of the world and harbors ∼1000 native freshwater fish species in different freshwater ecosystems ranging from small lakes, streams to large rivers (Anon, 2017). The major rivers of India are Ganga, Godavari, Krishna, Brahmaputra, Mahanadi, Narmada and Kaveri. The geological and tectonic adjustments have resulted in profound changes in the trajectories of these rivers. Accordingly, ecosystems of these rivers are unique and different from each other (Sinha et al., 2012). They harbor huge fish diversity and provide means of livelihood to the local people. Apart from Indian major carps, catfishes (Order: Siluriformes) have wide distribution in all these rivers (Ferraris and Runge, 1999). As per the biogeographical studies, catfishes are of Gondwanan in origin and rafted from Africa to Asia through vicariance and concomitant dispersal (Kappas et al., 2016).

Among catfishes, Sperata seenghala (Sykes, 1839), a member of the family Bagridae grows to ∼150 cm in length and is often known as “Giant river-catfish”. Initially, Sykes (1839) described this species as Platystoma seenghala. Later, the generic name of this species has undergone several replacements (Bagrus, Aorichthys, Mystus) and currently it is placed under the genus Sperata (Ferraris and Runge, 1999). As a carnivorous fish, it plays a predator role in ecosystem and prey on larvae, crustaceans, molluscs and worms (Saini et al., 2008; Arif, 2012). It is a demersal fish and breeds during the monsoon season (Raj, 1962). Apart from India, it is also distributed in Afghanistan, Bangladesh, Nepal and Pakistan (Talwar and Jhingran, 1991). Even though the conservation status of this fish is assessed as “Least concern” (Ng, 2010), studies have reported declining trend in population size in Bangladesh (Rahman et al., 2011).

In India, S. seenghala fetches higher price than carps due to its good taste and less intramuscular bones (Saini et al., 2008). It is a good source of trace metals (Zn and Fe) and is rich in essential amino acids (Mohanty et al., 2012). Although S. seenghala is a non-air breathing catfish, it can tolerate a wide range of temperature, salinity and water conditions (Sehgal, 1967). Few studies have attempted to develop culture practices for S. seenghala and also shown the species compatibility for polyculture with carps (Rahman et al., 2011, 2014). However, lack of standardized induced breeding technology hindered the expansion of S. seenghala culture (Rahman et al., 2005). Thus, the entire demand for this fish is being met by natural populations through capture, which may lead to population depletion or extirpation. Further, the anthropological factors have altered the ecology of its habitat through dam construction, domestic or industrial sewage release, sand mining and over exploitation (Saini et al., 2008). It could result in loss of fish breeding/nursery grounds and subsequent depletion of natural populations. Management measures informed by knowledge of genetic structure of populations would assist in restoring the depleted populations.

In S. seenghala, few studies have been carried out on the characterization of genetic structure of populations using morphology (Saini et al., 2008), Otolith (Miyan et al., 2016), RAPD (Saini et al., 2010) and mitochondrial DNA markers (Kumari et al., 2017). However, compared to morphological and mitochondrial DNA markers, nuclear microsatellite markers are the most popular for a wide range of applications in population genetics, conservation biology and evolutionary biology (Estoup and Angers, 1998). Due to their abundance in genomic DNA, codominant nature, high level of polymorphism and ease of screening, microsatellites have been widely used in the characterization of catfish stocks (Mandal et al., 2016; Nazir and Khan, 2017; Srivastava et al., 2017). To date, no study has employed microsatellites to characterize the genetic structure of populations of S. seenghala. The aim of the present study was to determine the number of distinct populations of S. seenghala within India based on microsatellite markers.

2 Material and methods

2.1 Sampling

A total of 150 individuals of S. seenghala, 30 each from five rivers namely Brahmaputra (Assam: 26°10ʹN 91°46ʹE), Ganga (West Bengal: 22°35ʹN 88°20ʹE), Godavari (Andhra Pradesh: 17°00ʹN 81°46ʹE), Mahanadi (Odisha: 21°28ʹN 83°58ʹE) and Narmada (Madhya Pradesh: 22°33ʺN 77°58ʹE)were collected during 2015–2016 (Fig. 1). Among these rivers, Ganga and Brahmaputra have connectivity through their tributaries before their flow into Bay of Bengal. Other peninsular rivers (Godavari and Narmada) do not have direct connectivity among them. However, they may have transient connectivity during monsoon season due to overlapping of the catchment areas of their tributaries. Sampling was performed at different time points to include individuals of different age groups (juvenile to adult fish) in a population. Dorsal fin was dissected aseptically, preserved in absolute alcohol and transported to the lab. The samples were stored at −20 °C until further use.

thumbnail Fig. 1

Map showing the sampling details of S. seenghala. River Brahmaputra and Ganga merge each other through tributaries before flow into Bay of Bengal.

2.2 Microsatellite analysis

Total genomic DNA was isolated from the fins using phenol–chloroform method (Sambrook et al., 2001). A total of 15 microsatellite markers developed earlier for S. seenghala were used for characterizing the genetic stocks (Acharya et al., 2018). Out of 15 microsatellites, the number of di-, tri-, tetra- and penta nucleotide repeats was 2, 7, 4 and 2, respectively. PCR was performed in 25 µl reaction volume containing 100ng template DNA, 1×Taq buffer with 1.5 mM MgCl2, 10 pmol of each specific primer, 200 µM of each dNTPs and 1.0 units of Taq DNA polymerase. The thermocycler was programmed for initial denaturation at 95 °C for 5 min, followed by 35 cycles at 94 °C for 30 s, primer-specific annealing temperature for 30 s, 72 °C for 1 min with final extension at 72 °C for 10 min. Hold temperature was set at 4 °C. The PCR products were separated on 10% polyacrylamide gel and then visualized by ethidium bromide staining. DNA ladders of 50 and 100 bp plus (Thermo Scientific, USA) were used to estimate the size of the amplicons. Alleles were scored as per amplicon size using MyImageTM analysis software v2.0 in-built in the Gel Doc system (Bio-Rad, USA).

2.3 Statistical analysis

The allele frequencies, average number of alleles (N a), mean effective number of alleles (N e), observed (H o) and expected heterozygosity values (H e) were estimated using GenALEx 6.4 (Peakall and Smouse, 2006). Polymorphic information content (PIC) values were estimated for all loci using Cervus v.3.0 (Marshall et al., 1998). Genotypic linkage disequilibrium between pairs of different loci and deviation from Hardy–Weinberg equilibrium (HWE) was investigated using Genepop version 4.0 (Rousset, 2008). Locus conformance to HWE was tested by Fisher's exact P test with Markov chain method using Genepop version 4.0. The significant criteria/values were adjusted for the number of simultaneous tests using sequential Bonferroni technique (Rice, 1989). The presence of null alleles was detected using Micro-Checker 2.2.3 (Van Oosterhout et al., 2004). The coefficient of genetic differentiation (F ST) and inbreeding coefficient (F IS) was calculated using Genepop version 4.0 (Wright, 1978; Weir and Cockerham, 1984). Pair-wise population F ST, R ST (Slatkin, 1995), Nei's genetic distance (Nei, 1972) genetic identity and their significant values were estimated by GenALEx 6.4. The hierarchical analysis of molecular variance (AMOVA) was performed using Arlequin version 3.0 (Excoffier et al., 2005). Genetic stock structuring was tested using a Bayesian model-based clustering method implemented in STRUCTURE v.2.3.4 (Pritchard et al., 2000). An admixture model with a uniform prior (α = 1, max = 10) and correlated allele frequency model was implied to assign individuals to their most likely genetic clusters (K). A range of K from 1 to 10 with 5 runs of each K value was examined. A series of simulations were run for 1 000 000 MCMC (Markov Chain Monte Carlo) replicates with a burn-in period of 100 000 iterations for each value of K to verify the stability of the results. The maximal average value of ln P(D) was used to estimate the most likely value of K (Evanno et al., 2005). Principal coordinate analysis (PCoA) was also employed to investigate the genetic relationships among populations using GenAlEx 6.4.1.

3 Results

Screening of 150 individuals (5 populations) at 15 loci revealed a total of 178 alleles. Locus-wise, the number of alleles varied from 8 (Sps 6) to 19 (Sps 13) with a mean of 12 alleles per locus. Highest and least number of alleles were recorded in Brahmaputra (84 no.) and Mahanadi (70 no.) populations, respectively. The mean number of alleles per locus was slightly higher in Brahmaputra (6 no.) than in other populations (5 no.) (Tab. 1). No significant linkage disequilibrium (P > 0.05) was observed among loci for any population. It confirmed that all microsatellite loci were not linked and assorted independently. Across loci, the PIC value ranged from 0.573 (Sps 14) to 0.858 (Sps 1) and it indicated the high polymorphic nature of microsatellites (Supplementary Tab. 1).

Null alleles were detected at loci Sps 3 (Ganga, Godavari), Sps 5 (Brahmaputra), Sps 7 (Brahmaputra) and Sps 10 (Ganga, Godavari) (Supplementary Tab. 2). The genotypes of these loci were adjusted using Micro-checker software and used for further population genetic analysis. The mean observed and expected heterozygosity values across populations varied from 0.622 to 0.699 and 0.733 to 0.774, respectively (Tab. 1). All populations deviated from HWE at several loci with positive F IS values even after sequential Bonferroni corrections (P < 0.05). Brahmaputra and Godavari populations were deviated from HWE at locus Sps 5 and Sps 3, respectively. In case of Ganga population, 9 of 15 loci (Sps 1–5, Sps 8–10 and Sps 14) were deviated from HWE and the F IS values were in the range of 0.133 (Sps 5) to 0.467 (Sps 14). Individuals of Mahanadi populations were deviated from HWE at 4 loci (Sps 4, Sps 6, Sps 11 and Sps 13). The correspondent F IS values were distributed from 0.178 (Sps 4) to 0.250 (Sps 11). Narmada population deviated from HWE at three loci with F IS values of 0.151 (Sps 5), 0.159 (Sps 6) and 0.146 (Sps 9) (Tab. 1). The positive values indicated the possible violation of assumptions basic to HWE and indicated the deficiency of heterozygotes.

Both F ST and R ST values showed significant differentiation among populations (P < 0.05). Pair-wise F ST values varied from 0.135 (Brahmaputra–Ganga) to 0.173 (Brahmaputra–Narmada). Accordingly, pair-wise R ST values ranged from 0.558 (Brahmaputra–Ganga) to 0.775 (Brahmaputra–Narmada) (Tab. 2). Low genetic distance and high genetic identity were observed between Brahmaputra and Ganga populations (GD: 0.464, GI: 0.523). High genetic distance and less genetic identity values were observed between Brahmaputra and Narmada populations (GD: 0.765; GI: 0.222) (Tab. 3).

The AMOVA test revealed significant genetic differentiation among the populations of S. seenghala. A value of 82% was observed within individuals, whereas 15% of the variation was observed among populations (F ST 0.157; P < 0.001) (Tab. 4). The PCoA plot grouped individuals into distinct clusters corresponding to their populations (Fig. 2). Clusters representing populations of Ganga and Brahmaputra were relatively closer than other clusters. Bayesian clustering analysis using STRUCTURE showed five clusters/groups as the most likely population structure (Fig. 3). The highest value of ln P(D) was observed for K = 5, which indicated that all samples were divided into five groups.

A considerable proportion of alleles (36%) were identified as private alleles (65 no.) across all populations. The highest number of private alleles were observed in Godavari (17 no.) followed by Brahmaputra (15 no.), Mahanadi (13 no.) and Ganga (11 no.) (Supplementary Tab. 3)

Table 1

Statistics for genetic variation in five populations of S. seenghala using 15 microsatellite loci.

Table 2

Pair-wise F ST (below diagonal) and R ST (above diagonal) values among five populations of S. seenghala with 15 microsatellite loci.

Table 3

Nei's genetic distance (below diagonal) and genetic identity (above diagonal) values among five populations of S. seenghala with 15 microsatellite loci.

Table 4

Analysis of molecular variance (AMOVA) based on microsatellite markers in five populations of S. seenghala.

thumbnail Fig. 2

Scatter diagram based on PCoA of distance variables between populations of S. seenghala.

thumbnail Fig. 3

Bayesian model-based clustering using of S. seenghala. STRUCTURE analysis as inferred at K = 5 based on microsatellite data. The genotype of each individual is represented by a vertical line divided into colored segments.

4 Discussion

Genetic diversity is an important aspect of population dynamics and essential for perpetual existence of a population. It acts as a raw material upon which the species/population depends to adapt to ever changing environmental/climate conditions. The average number of alleles per locus is often considered as a preliminary indicator about the genetic diversity of the population. In the present study, with a sample size of 150 S. seenghala individuals, the mean observed (N a) and effective number (N e) of alleles per locus was 12 and 9.85, respectively. In Sperata aor, Nazir and Khan (2017) recorded mean values of N a (20) and N e (13.27) with a sample size of 280 individuals. Mandal et al. (2016) recorded relatively high average N a value of 11 in 76 individuals of S. silonida. Allele richness is considered as one of the diversity indices of a population (Foulley and Ollivier, 2006). In the present study, population-wise, allelic richness was relatively high in Brahmaputra population (6 no.) followed by Godavari, Mahanadi, Narmada and Ganga (5 no.). However, the allelic richness values vary with the sample size and in the present study the optimum sample size of 30 individuals/population were taken for analyses (Ruzzante et al., 1998; Hale et al., 2012).

PIC is the ability of a marker to reveal the polymorphism at a particular locus in a population. This value could also be used as an indicator of the genetic diversity in a population at a particular locus. In the present study, all loci showed high values of PIC (>0.5) indicating potentiality of these markers in genetic diversity studies of S. seenghala. Several previous studies also reported high PIC values for microsatellites in catfishes (Mandal et al., 2016; Srivastava et al., 2017).

In the present study, null alleles were observed at 4 loci among different populations. Reasons for null alleles are preferential amplification of short alleles, poor quality and quantity of template DNA and enzyme slippage during PCR (Van Oosterhout et al., 2004). Previous studies have reported null alleles in other catfishes (Perales-Flores et al., 2007; Mandal et al., 2016). Chapuis and Estoup (2007) reported that null alleles with frequencies between 0.05 and 0.08 would have minor effects on classical estimates of population differentiation. In the present study, the null allele's frequency was in the range of 0.0814–0.0998. However, these genotypes were adjusted with Micro-checker software to nullify the null allele effect on further downstream analysis (Van Oosterhout et al., 2004). Further, genetic differentiation detected after correction for null alleles was similar as that obtained with uncorrected data.

In the present study, the mean observed heterozygosity (H 0) values ranged from 0.622 (Ganga) to 0.699 (Godavari) and revealed relatively high genetic diversity within S. seenghala populations. Fishes with large population size that migrate during breeding season were reported to display high level of genetic diversity (Santos et al., 2007). S. seenghala breeds during monsoon season and the increased water level in respective water bodies/rivers could assist in migration of this fish from lotic to lentic water bodies (Ranganathan and Natarajan, 1978). Probably, it would result in minimizing the effect of population genetic drift. Previous study on Sperata aor recorded high values of observed heterozygosity (0.956–0.980) than the present study (Nazir and Khan, 2017).

In this study, all populations were deviated from HWE at several loci. Multiple factors such as inbreeding, population admixture (Wahlund effect), genetic drift (in case of small populations), bottleneck effects, founder events, mutations, genotyping error, null alleles and selection can cause deviation of population from HWE (Gibbs et al., 1997; Hosking et al., 2004). The probable reason for HWE deviation could be ascertained by observing inbreeding coefficient values (F IS).The positive F IS values observed in the present study indicated deficiency of heterozygotes. This deficiency could also be due to mixing of unexplored genetically divergent populations within the samples, a phenomenon known as Walhund effect (Hartl and Clark, 1997). S. seenghala migrates to the wetlands that are adjacent to rivers and these water bodies disconnect from the river after post-monsoon (Saigal and Motwani, 1961). Their connectivity to the main river depends on the intensity of monsoon season. Prolonged disconnectivity would lead to altering the genetic makeup of these samples. In the present study, population of Godavari, Narmada, Mahanadi and Brahmaputra showed deficiency in heterozygotes at few loci as per the HWE. Probably these populations would have samples from different populations of S. seenghala which could have been from adjacent wetlands of rivers.

Relatively, Ganga population showed more loci with positive F IS values (9). It could be due to reduction in heterozygotes in S. seenghlala populations due to overexploitation and adverse ecological changes. S. seenghala prefers moderately high water temperatures, sluggish water current and sandy bed for breeding (Sathyanesan, 1962). River Ganga ecosystem is being threatened due to illegal sand mining, release of untreated industrial waste, deforestation in the catchment area and construction of dam (Nazir and Khan, 2017). These factors could lead to reduction of S. seenghala population size and subsequent heterozygotes. However, further study with wide sampling coverage across the river is required for substantial evidence/confirmation. Several previous studies also reported the non-conformity of HWE in populations of catfishes Silionia silondia, Sperata aor and Pelteobagrus fulvidraco (Hu et al., 2009; Mandal et al., 2016; Nazir and Khan, 2017).

In the present study, population differentiation was estimated using F ST and R ST statistics. Both F ST and R ST measure the connectivity and patterns of gene flow among populations. F ST estimate assumes infinite allele model (IAM) and depends on mutation rate and number of migrants. While R ST is independent of the mutation rate and relies on stepwise mutation model (SMM). F ST estimates are sensitive to mutations rate when migration is low (Balloux and Lugon-Moulin, 2002). In the present study, pair-wise F ST values ranged from 0.135 (Godavari–Narmada) to 0.173 (Brahmaputra–Narmada) and showed moderate to high genetic differentiation among the populations (Wright, 1978; Hartl and Clark, 1997). Initially, catfishes have originated in Africa and drifted to other parts of the world through continental drift during Mesozoic and Cenozoic era (Diogo, 2004). During the trajectory of Indian subcontinent toward Eurasian plate, due to several geological changes, rivers Godavari, Narmada and Mahanadi were formed (Biswas, 1999). River Godavari and Narmada had several tributaries whose water catchment area would be overlapping during monsoon floods. During this time, these rivers would have connectivity through intermediary streams/wetlands and it could have facilitated gene flow between these rivers. Accordingly, relatively less F ST value (0.140) was observed between Godavari–Narmada populations. However, later the connectivity could have lost due to local geological changes and led to structuring of fish populations.

Low genetic divergence and high genetic identity was observed between Ganga–Brahmaputra populations. These Himalayan rivers merge with each other before flowing into the Bay of Bengal (Allison, 1998). S. seenghala is a hardy species and can tolerate moderate changes in ecosystem (Sehgal, 1967). It could migrate between Ganga and Brahmaputra rivers through the river confluence and resulting in low genetic distance between these rivers. Pair-wise R ST values were slightly higher than F ST values and confirmed high genetic variation among populations. Higher R ST value than F ST indicates high differentiation among populations due to mutations than migration (Lugon-Moulin et al., 1999). Kumari et al. (2017) showed high genetic differentiation among the populations of S. seenghala using mitochondrial D-loop and cytochrome b genes.

AMOVA, STRUCTUE and PCoA showed significant genetic differentiation among the sampled populations of S. seenghala. The cluster that includes Ganga and Godavari was relatively close to Brahmaputra and Narmada, respectively. It confirmed the existence of distinct genetic populations among different rivers and signifies the need for separate population management. The genetic distinctness of a population depends on the number of unique alleles (private alleles) present in the population (Kalinowski, 2004). The occurrence of private alleles was related to the mean number of migrants exchanged per generation between populations and the frequency of private alleles would increase when number of migrants is low between populations (Slatkin, 1985; Szpiech and Rosenberg, 2011). In the present study, a total of 65 private alleles confined to different populations were observed. The samples from Godavari population showed a high number of population specific alleles. These alleles could be used as population specific genetic signatures for assigning individuals to the respective populations and selective breeding programme.

5 Conclusion

In conclusion, the present study characterized the population genetic structure of S. seenghala from five major rivers of India and showed considerable genetic diversity within each population and genetic distinctiveness of each population. However, population from Ganga river showed excess homozygotes and warrants for conservation measures. The present study results could be useful to formulate sustainable management and conservation measures for S. seenghala. Further studies are required to assess the genetic structuring of S. seenghala within each river.

Acknowledgements

The first author acknowledges Department of Science and Technology, New Delhi, India for providing fellowship to carry out the research. This work has been carried out under the ICAR Network project entitled, “Outreach Activity on Fish Genetic Stocks (Phase II)”.

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Cite this article as: Acharya AP, Pavan-Kumar A, Gireesh-Babu P, Joshi CG, Chaudhari A, Krishna G. 2019. Population genetics of Indian giant river-catfish, Sperata seenghala (Sykes, 1839) using microsatellite markers. Aquat. Living Resour. 32: 4

All Tables

Table 1

Statistics for genetic variation in five populations of S. seenghala using 15 microsatellite loci.

In the text
Table 2

Pair-wise F ST (below diagonal) and R ST (above diagonal) values among five populations of S. seenghala with 15 microsatellite loci.

In the text
Table 3

Nei's genetic distance (below diagonal) and genetic identity (above diagonal) values among five populations of S. seenghala with 15 microsatellite loci.

In the text
Table 4

Analysis of molecular variance (AMOVA) based on microsatellite markers in five populations of S. seenghala.

In the text

All Figures

thumbnail Fig. 1

Map showing the sampling details of S. seenghala. River Brahmaputra and Ganga merge each other through tributaries before flow into Bay of Bengal.
In the text
thumbnail Fig. 2

Scatter diagram based on PCoA of distance variables between populations of S. seenghala.

In the text
thumbnail Fig. 3

Bayesian model-based clustering using of S. seenghala. STRUCTURE analysis as inferred at K = 5 based on microsatellite data. The genotype of each individual is represented by a vertical line divided into colored segments.
In the text

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