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Genetic structure of populations and conservation issues relating to an endangered catfish, Clarias batrachus, in India ARTICLE in MITOCHONDRIAL DNA · JULY 2014 Impact Factor: 1.21 · DOI: 10.3109/19401736.2014.945524

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http://informahealthcare.com/mdn ISSN: 1940-1736 (print), 1940-1744 (electronic) Mitochondrial DNA, Early Online: 1–7 ! 2014 Informa UK Ltd. DOI: 10.3109/19401736.2014.945524

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Genetic structure of populations and conservation issues relating to an endangered catfish, Clarias batrachus, in India Gulab D. Khedkar1,2, Anita Tiknaik1, Amol D. Kalyankar1, Chandra Sekhar Reddy A3, Chandraprakash D. Khedkar4, Tetsuzan Benny Ron5, and David Haymer2 1

Paul Hebert Centre for DNA Barcoding and Biodiversity Studies, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India, Department of Cell and Molecular Biology, University of Hawaii, Honolulu, HI, USA, 3State Institute of Fisheries Technology, PCR Lab, Jagannaickpur, Kakinada, Andhra Pradesh, India, 4College of Dairy Technology, Maharashtra Animal and Fisheries Sciences University, Nagpur, Maharashtra, India, and 5Department of Human Nutrition, Food and Animal Science, College of Tropical Agriculture and Human Resources, University of Hawaii at Manoa, Honolulu, HI, USA

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Abstract

Keywords

The Asian catfish, Clarias batrachus (Linnaeus, 1758), is a highly valued species endemic to India that is currently in drastic decline in most of its natural habitat. The present study was undertaken to document the genetic structure of populations of this species using mitochondrial DNA markers, specifically from the cytochrome B and D-loop regions. Specimens from eight wild populations were collected and analyzed from different regions in India. The genetic variation within and among populations was evaluated using a range of descriptive statistics. The analysis described here provides a broad and consistent view of population structure and demographic history of populations of C. batrachus. Although there was some genetic structuring consistent with regional differences, all eight populations examined here showed relatively low levels of genetic variation in terms of both haplotype and nucleotide diversities in the different analyses used. However, a number of private haplotypes were discovered, and this may provide valuable information for future selective breeding program and conservation management. The results may aid in the design and implementation of strategies for the future management of this endangered catfish C. batrachus in India.

Breeding, Clarias batrachus, Cyt B, Dloop, genetic diversity, haplotype, India

Introduction The silurid catfish, Clarias batrachus (Linnaeus, 1758) commonly known as ‘‘Magur’’, is a native fish of India found in almost all its river systems, lakes and ponds. This fish represents one of the important food sources in India. In addition, it has high market demand due to its taste and medicinal properties (Debnath, 2011; Hossain et al., 2006). It is also attractive to consumers since the fish can remain fresh and alive out of water for a considerable time due to its air-breathing nature. However, it is well known that C. batrachus populations have been drastically reduced during last two decades due to over fishing and habitat alterations (Khedkar et al., 2009). During the 1980s, farmers in West Bengal attempted to develop technologies for seed production and aquaculture of this species to fulfill market demand. To some extent this was successful, but the low fecundity of the females coupled with early mortality during seed production became major bottlenecks associated with the aquaculturing of this fish in India (Hossain et al., 2006). To meet the continuing demand for supply of C. batrachus, a morphologically similar fish, C. garipinus (Burchell, 1822),

Correspondence: Gulab D. Khedkar, Paul Hebert Centre for DNA Barcoding and Biodiversity Studies, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad – 431004, Maharashtra, India. E-mail: [email protected]

History Received 16 April 2014 Revised 10 July 2014 Accepted 13 July 2014 Published online 8 August 2014

attracted the attention of aquaculturists and paved the way for its unauthorized introduction in Indian waters (Thakur, 1996). However, once introduced, the aggressive carnivorous nature and rapid growth rate of C. garipinus promoted its establishment in almost all river systems in India. This also apparently led to further drastic declines in populations of C. batrachus (Khedkar et al., 2009, 2014a; Thakur, 1996). Over harvesting of C. batrachus populations also continued to have an impact on the genetic diversity of this species. Early studies suggested a lack of genetic diversity of C. batrachus in India (Khedkar et al., 2009) and resulted in this fish being given a ‘‘Critically Endangered’’ listing on the IUCN (IUCN, 2007) Red List of Threatened Species (Argungu et al., 2013). This listing reinforces the major conservation concerns for this fish in India. For decisions regarding conservation of such threatened species, baseline information on the genetic composition and structure of populations is necessary for many reasons, including the definition of areas for management (Birstein et al., 2005; Ludwig et al., 2009; Stephenson & Kenchington, 2000). Also for such species, assessments of total genetic variation split into variation within and among populations and at geographic levels such as between river habitats and among river systems, can be tremendously helpful for management and conservation decisions. A wide array of techniques is available for such population genetic analyses (Avise, 1994; Hillis et al., 1996; Hoelzel, 1998; Smith & Wayne 1996). Mitochondrial DNA sequences, in particular, have a number of advantages that make them

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G. D. Khedkar et al.

informative for studies of population level variation and differentiation, especially when more than one such marker is used. Therefore, to infer the genetic structure of C. batrachus, we have used two distinct regions of the mitochondrial DNA, specifically the ‘‘D-loop’’ hyper variable control region and cytochrome oxidase-B gene for the population level studies.

Materials and methods Ethical statement


We declare that, the fish under study are not protected under wild life conservation act and are routinely caught by fisherman and sold as a food fish in Indian markets. No specific permit is required for obtaining these fish in India, and no experimentation was conducted on live specimens in the laboratory. The fish specimens of C. batrachus in the range of sub-adult to adult size were purchased from fishers engaged in commercial fishing (Figure 1). DNA isolation, PCR and sequencing From each specimen a small fin clip (5 sq. mm) was preserved in absolute ethanol and used for DNA isolation using the Promega Wizard DNA isolation kit (Promega Corporation, Madison, WI). Oligonucleotides used to amplify a partial region of mitochondrial Cyt b gene were, L14841 (Kocher et al., 1989): (50 -AAC AAG CTT CCA TCC AAC ATC TCA GCA TGA TGA AA-30 ) and H15630: (50 -TTA ATT TAG AAT CCT AGC TTT GG-30 ), whereas D-loop fragments were amplified using the primer L16473: 50 -CTA AAA GCA TCG GTC TTG TAA TCC-30 and H355: 50 -CCT GAA ATG AGG AAC CAG ATG-30 . Figure 1. Map of India showing sampling sites (shaded area is river basin area and black dots are sampling sites) and approximate distribution area of Clarias batrachus.

Mitochondrial DNA, Early Online: 1–7

DNA amplification was conducted in gradient thermo cycler (ABI Verity, PCR system, Foster City, CA) using 5 PCR Buffer, 25 mM MgCl2, 10 mM dNTP, 5 mM of each primer, 25 ng (2.5 ml) of template DNA, 5U of Taq DNA Polymerase (Invitrogen Corporation, Carlsbad, CA) and mili Q water to 25 ml. For the Cyt b amplification, an initial denaturation at 94 C (2 min) was used followed by 30 cycles of denaturation at 94 C (5 min), annealing at 51 C (1 min 10 s), extension at 72 C (3 min) and final extension at 70 C (5 min). For the D-loop sequences, PCR amplification was programmed at 94 C for 2 min followed by 29 cycles (94 C for 1 min, 56 for 1 min 10 s and 72 C for 2 min) and a final extension of 5 min at 72 C. Successful amplicons were purified and subjected to cycle sequencing using the ABI Prism BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems, Foster City, CA), following the protocol suggested in the kit instructions and employed for sequencing. Sequencing products were subjected to capillary electrophoresis in the ABI 3130 genetic analyser (Applied Biosystems). Fragments were sequenced on both DNA strands to ensure accurate data collection. Sequence files were aligned with the aid of the program Codon code Aligner v.3.0 (CodonCode Corporation, www.codoncode.com). All sequences have been deposited in GenBank under the following inclusive accession numbers: KF938772–KF938885. Molecular analyses The genetic variation within and among populations was evaluated using empirical descriptive values such as haplotype diversity (h), nucleotide diversity (p) (Nei, 1987) and segregating

Genetic structure of an endangered catfish in India

DOI: 10.3109/19401736.2014.945524


sites (S) (Nei & Kumar, 2000). Population stasis was evaluated using Fu’s F (Fu, 1997) using DnaSP version 4.50.2 (http:// www.ub.edu/dnasp/DnaSP_OS.html). Similarly Tajima’s D was used to test against selective neutrality and population equilibrium (Tajima, 1989). The mismatch distribution bias was also used for testing the hypothesis of population expansion (Slatkin & Hudson, 1991). The fractions of the total genetic variation distributed among populations were estimated using the Fst statistic (Lynch & Crease, 1990). AMOVA was used for the computation of these parameters (Excoffier et al., 1992). To obtain confidence intervals for the Fst estimates, one million non-parametric permutations of haplotype distribution were performed. The calculations of all the above descriptors were performed with the aid of the computer program DnaSP (Rozas et al., 2003) and ARLEQUIN ver. 3.0 (Excoffier et al., 2005). A minimum-spanning (parsimony-based) network for the haplotypes was inferred as an alternative to the ML and the Bayesian trees. This network was recovered using the program TCS v. 1.21 (Clement et al., 2000).

Results In this study DNA sequences were analyzed from specimens collected from eight different populations within India. The populations studied were grouped by geographic region as shown in Figure 1. Within these populations, DNA sequences were obtained from the cytochrome B (1060 bp) and D-loop regions (522 bp) from a total of 179 individuals. Concatenated assemblies of 1582 bp (average length) were used for the analyses described here. A total of 51 polymorphic sites including 47 singletons and 4 parsimony informative sites were detected within this dataset. Genetic diversity A total of 29 different haplotypes were identified. Their distribution and relative frequencies by population are shown in

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Table 1. Overall haplotype diversity (h) and nucleotide diversity () values were 0.5169 and 0.0532, respectively. Population-wise haplotype diversity values ranged from 0.000 to 0.7544 and values for nucleotide diversity ranged from 0.00 to 0.0216 (Table 2). In general, the populations from the Narmada, the Shivna, the Godawari and the Purna river are the least diverse regions in terms of haplotype and nucleotide diversity, while the Girna and the Ganga populations exhibited relatively high diversity values (Table 2). Gene flow inferred from Fst values Pairwise Fst values (Table 3) between populations ranged from 0.06641 to 0.94301. More than half (17) of these comparisons yielded significant values. From the results of the ANOVA, a slight majority of the variation was found among groups compared to that found within populations (Table 4). To consider gene flow among different populations, we conducted further analyses in ARLEQUIN and obtained M values (Table 5). Almost 50% of the M values are greater than two, and few were infinite, suggesting that there is considerable gene flow between these populations. Phylogeographic and demographic history From the haplotype distribution it can be seen that haplotype 5 is widespread, being found in almost 68% of the 121 individuals surveyed here. This is likely to be the ancestral variant according to coalescent theory (Posada & Crandall, 2001). Haplotype 1 is shared by 24 (13.40%) of the individuals from three populations while the other 27 haplotypes were represented by single individuals (Table 1). Parsimony analysis of the haplotype relationships produced a star-shaped pattern with a common, central haplotype differing from most of the other haplotypes by only one or two nucleotide differences. This suggests that there has been limited differentiation of these populations (Figure 2).

Table 1. Haplotype distribution and frequency. Population Haplotype Hap 1 Hap 2 Hap 3 Hap 4 Hap 5 Hap 6 Hap 7 Hap 8 Hap 9 Hap 10 Hap 11 Hap 12 Hap 13 Hap 14 Hap 15 Hap 16 Hap 17 Hap 18 Hap 19 Hap 20 Hap 21 Hap 22 Hap 23 Hap 24 Hap 25 Hap 26 Hap 27 Hap 28 Hap 29

Ganga 19 2 1 1 1 1 1

26 (0.731) (0.0769) (0.0385) (0.0385) (0.0385) (0.0385) (0.0385) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Girna 19 4 (0.211) 0 0 0 9 (0.474) 0 0 1 (0.0526) 1 (0.0526) 1 (0.0526) 1 (0.0526) 1 (0.0526) 1 (0.0526) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Kakinada

21

1 1 1 1 1

26 0 0 0 0 (0.808) 0 0 0 0 0 0 0 0 (0.0385) (0.0385) (0.0385) (0.0385) (0.0385) 0 0 0 0 0 0 0 0 0 0 0

Kanhanan

24

1 1 1 1 1

29 0 0 0 0 (0.828) 0 0 0 0 0 0 0 0 0 0 0 0 0 (0.0345) (0.0345) (0.0345) (0.0345) (0.0345) 0 0 0 0 0 0

Shivna 22 0 0 0 0 20 (0.909) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 (0.0455) 1 (0.0455) 0 0 0

Godavari

20

1 1 1

23 0 0 0 0 (0.87) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 (0.0435) (0.0435) (0.0435)

Narmada

Purna

1 1 (1.0) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

27 0 0 0 0 26 (0.963) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 (0.037) 0 0 0 0 0


27 1 2 0.0741 ± 0.0674 0.00145 ± 0.0032 7 0 1 0.0000 ± 0.0000 0.0000 ± 0.0000 29 16 6 0.3202 ± 0.1116 0.02163 ± 0.01622 No. of Samples (n) No. of Polymorphic sites (PS) No. of Haplotypes (k) Haplotype diversity (h) Nucleotide diversity ()

26 10 7 0.4708 ± 0.1194 0.0164 ± 0.01347

19 6 8 0.7544 ± 0.0904 0.0201 ± 0.01574

26 6 6 0.3538 ± 0.1194 0.01049 ± 0.01003

22 3 2 0.1775 ± 0.1062 0.0071 ± 0.0079

23 10 4 0.2490 ± 0.1165 0.0170 ± 0.0138

Purna Narmada Godawari Kanhanan Kakinada Ganga Parameters

Table 2. Genetic diversity of C. batrachus populations.

Girna

Sampling stations

Shivna


179 51 29 0.5169 0.0532


Overall analysis

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For these populations the hypothesis of constant population size was rejected by Fu’s Fs (39.453; p50.001). Likewise, Tajima’s D rejected a scenario of selective neutrality and population equilibrium (2.6150; p50.001). Fu and Li’s D* (9.98078, p50.002) and F* (8.18232, p ¼ 0.02) were both negative and significant, supporting the results of the analysis based on Fu’s Fs and Tajima’s D (Table 6). The unimodal mismatch distribution suggests that C. batrachus populations have undergone periods of population growth (SSD ¼ 0.01034, p ¼ 0.25) (Figure 3) although now it is scanty. This was also confirmed by the raggedness index test that failed to reject the hypothesis of purifying selection (r: 0.1125, p ¼ 0.21). The absence of any obvious geographic pattern in the haplotype distribution produced by the TCS analysis also suggests limited phylogeographic structuring of these populations of C. batrachus in India (Figure 2).

Discussion The results of this study have revealed several findings in relation to the genetic structure of C. batrachus populations in India. The mitochondrial DNA markers used here (cytochrome B and D-loop) showed varying levels of genetic structuring across the sampling locations studied. Within the geographically defined groups, most populations were characterized by low values for both haplotype and nucleotide diversity. Also found was a single common, widespread haplotype and a unimodal-shaped mismatch distribution. Overall, the data show the presence of relatively little genetic diversity among these populations. These results may reflect the impact of a number of different factors including over fishing and recent founder effects. It is also of note that the Purna, the Shivna and the Godavari populations have low nucleotide and haplotype diversities even though they belong to a common watershed basin within the Godavari river. The pattern of genetic variation in these areas may reflect the impact of various barriers such as dams and weirs, as well as founder effects resulting from different colonization events. In other studies, the patterns of genetic diversity indices seen for mtDNA have also been shown to be related to parameters such as effective population size and/or selection effects for both nuclear and mitochondrial loci and genomes (Brito, 2007; Burg & Croxall, 2001). Demographic history Pairwise population comparisons with low Fst values show a pattern that may reflect extensive gene flow, especially between the Ganga and the Narmada populations as well as the Godavari and the Kanhanan populations, although some increased genetic divergence is observed between the Narmada and the Purna populations. Using M values, estimates of gene flow also strongly correlate with the geographic areas defined. For example, the Ganga and the Narmada populations are from a common basin area (the Northern region), and they show extensive mixing. The six populations from the southern region of India may also be genetically connected through gene flow (Table 6). However, the populations from the southern region appear to show a greater degree of isolation from the northern group of populations. Gene flow between Southern and Northern regions may be restricted by many factors including habitat and landscape barriers. This represents an important finding that may have implications for future aquaculture activities involving this species because major seed production regions for C. batrachus are in the Kakinada area for the southern region and in the Kolkotta area for rest of the country. For fish species such as C. batrachus that are important candidates for aquaculture, habitat connectivity due to


DOI: 10.3109/19401736.2014.945524

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Table 3. Pairwise Fst vlaues below the diagonal and p values above diagonal.

Ganga Girna Kakinada Kanhanan Shivna Godavari Narmada Purna

Ganga

Girna

Kakinada

Kanhanan

Shivna

Godavari

Narmada

Purna

– 0.34575* 0.57429* 0.48594* 0.59758* 0.52033* 0.06641 0.67336*

0.000 – 0.08557* 0.05592* 0.09053* 0.06038* 0.38812* 0.13403*

0.000 0.000 – 0.00046 0.00202 0.00345 0.69258* 0.0062

0.000 0.000 0.423 – 0.00529 0.00107 0.50805* 0.00221

0.000 0.0090 0.3603 0.8468 – 0.00082 0.77633* 0.00608

0.000 0.000 0.1621 0.6276 0.7297 – 0.58301* 0.00593

0.99099 0.000 0.000 0.000 0.000 0.000 – 0.94301*

0.000 0.000 0.0630 0.9369 0.3243 0.1261 0.000 –

*Statistically significant (p50.005).


Table 4. AMOVA design and results (Confidence interval p50.005). Source of variation

d.f.

Sum of squares

Variance components

Percentage of variation

Among groups Among populations within groups Within populations Total

1 6 171 178

23.912 2.849 56.25 83.011

0.43532 Va 0.00662 Vb 0.32895 Vc 0.77088

56.47 0.86 42.67

Table 5. M values (absolute number of migrants per generation).

Ganga Girna Kakinada Kanhanan Shivna Godavari Narmada Purna

Ganga

Girna

Kakinada

– 0.94612 0.37065 0.52893 0.33670 0.46092 Infinity 0.24254

– 5.34308 8.44139 5.02295 7.78034 0.78825 3.23044

– 1079.341 247.044 144.455 0.22194 80.120

Kanhanan

Shivna

Godavari

Narmada

Purna

– Infinity 0.14406 81.73256

– 0.35762 83.86553

– 0.03021

–

– Infinity 0.48416 Infinity

Figure 2. Statistical parsimony network of mtDNA haplotypes. The size of the circle indicates the number of individuals with that haplotype. Hap5 was present in all the sampled populations with the exception of the Narmada river. Black circles are inferred haplotypes.

human translocations either through seed ranching and/or accidental escapes from culture facilities may play important roles in the maintenance of diversity. In general, though, many factors such as over exploitation, habitat fragmentation and other human activities relating to

culturing of this species in river basin areas all may have contributed to this lack of genetic diversity (Khedkar et al., 2009, 2014a,b; Swenson & Howard, 2004; Thakur 1996). The star-shaped pattern as seen in the haplotype network (Figure 2) and the mismatch analysis (Figure 3) confirm the

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Table 6. Population expansion estimates. Statistics

Ganga


Demographic expansion SSD 0.00806 SSD p value 0.26 Neutrality test Raggedness index 0.13374 p value 0.57 Fu’s F 3.66145 Fu’s F p value 0.001 Tajima’s D 2.22358 Tajima’s D p value 0.00100

Girna

Kakinada

0.02723 0.19

0.00452 0.59

0.18419 0.11 5.05156 0.001 1.29629 0.09300

0.22178 0.55 3.90876 0 1.95206 0.01200

Kanhanan 0.0158 0.33 0.33417 0.52 1.39933 0.157 2.49343 0.00000

Figure 3. Mismatch distributions of pairwise nucleotide differences between mtDNA haplotypes revealed a unimodal pattern and no significant differences between the observed and expected frequencies (sum of squared deviation ¼ 0.0082, p ¼ 0.25).

nature of the population relationships similar to what has been seen in other studies (Ball & Avise, 1992; Briggs, 2003; McCromack et al., 2008; Slatkin & Hudson, 1991) including the absence of divergent haplotypes between the geographic groups defined on a regional basis (Ali et al., 2005). In addition to gene flow, selective sweeps at one locus favoring a new advantageous allele spreading throughout the population may have also reduced the overall genetic variation at linked ‘‘neutral’’ loci (Ballard & Whitlock, 2004; Gershoni et al., 2009). The results of the analyses using Fu and Li’s D* and F* do not rule out background selection. Another possibility is a population bottleneck where a reduction in habitat and resources availability during the last century might have resulted in a significant decrease in the genetic variation (Craul et al., 2009; Goossens et al., 2006; Olivieri et al., 2008; Quemere et al., 2009; Sousa et al., 2009). However, it is important to note that the unimodal mismatch distribution is also consistent with a model of recent expansion, and an increase in effective population size (Hoelzel, 1998; Hooper et al., 2005; Rogers & Harpending, 1992). More support for this possibility comes from the neutrality test, where Fu’s Fs and Tajima’s D rejected the null hypothesis of population stability. Population relationships The mtDNA data obtained here supports the possible presence of at least two genetically distinct groups or metapopulations represented by the Southern and the Northern regions. It is possible that the reduction in gene flow detected in both of these metapopulations is due to geographical isolation. Also, the results for some specific cases such as the Narmada population, which

Shivna

Godawari

Narmada

Purna

Overall analysis

0.00982 0.24

0.01731 0.21

0 0

0.00002 0.24

0.01034 –

0.56337 0.66 0.73574 0.219 1.87763 0.01200

0.46906 0.57 0.18701 0.426 2.28970 0.00200

0 0 0

0.73114 0.88 1.12456 0.072 1.15354 0.13300

0.1125 – 39.453 2.00855 2.61506 0.001

N.A. 0.00000 1.00000

has the lowest genetic diversity of all the populations considered here and lacks any unique genetic variants, suggests that it might have been recently colonized from other populations. In addition, our results showing limited phylogeographic structure in C. batrachus using these mitochondrial markers is also supported by a previous study using RAPD genetic markers (Khedkar et al., 2009). However, the genetic relationships of non-sampled populations in other regions, such as the Northeast regions, is unknown. Finally, although all eight populations examined here were relatively homogeneous, the presence of several private haplotypes may reflect local adaptations of evolving populations. As such, it appears that all these populations (except perhaps the Narmada population) should be managed independently. For conservation purposes, ideally all available haplotypes should be included for broodstock development, and due care should be taken in maintaining the genetic integrity of each population. Artificial admixtures should be prevented. It is also important that more extensive studies be conducted on a wider sampling area using more variable molecular markers such as microsatellites to establish a clearer picture of the genetic structure.

Conclusions The mitochondrial markers used here provide a broad and consistent view of population structure and demographic history of populations of the catfish, C. batrachus, in India. Although there was some genetic structuring consistent with regional differences, all eight populations examined here showed relatively low levels of genetic variation based on the analyses used. Our results also provide insights into events that appear to have shaped the current genetic pattern of catfish populations from India. This will certainly aid in the design and implementation of strategies for exploiting available haplotypes for the future conservation management of this Endangered catfish in India.

Acknowledgements Authors are thankful to the staff of PHCDBS for their valuable support for conducting this work.

Declaration of interest All the authors declare that they have no competing interests. Authors like to thank the Department of Biotechnology, Government of India, for financial support to this project under File No.102/IFD/SAN/4469/20112012 Dated Feb 17, 2012 and F. No. BT/HRD/03/01/2002- Vol. III dated April 12, 2012. GDK – Experimental design, data analysis, funding for laboratory supplies, salaries and Manuscript writing. ADT – Molecular marker analysis; ADK, Collection of population, lab works. CDK – Experimental design, data analysis and manuscript writing; ACR, Animal collection, experimental design and data analysis. TBR – Experimental design, data

DOI: 10.3109/19401736.2014.945524

analysis and manuscript writing. DH – Experimental design, Data analysis and manuscript writing.


References Ali J, Jonathan R, Aitchison C. (2005). Greater India. Earth-Sci Rev 72: 170–3. Argungu LA, Christianus A, Amin SMN, Daud SK, Siraj SS, Aminur Rahman M. (2013). Asian catfish Clarias batrachus (Linnaeus, 1758) getting critically endangered. Asi J Ani Vet Adva 8:168–76. Avise JC. (1994). Molecular markers, natural history and evolution. New York: Chapman & Hall 511 pp. Ball RM, Avise JC. (1992). Mitochondrial DNA phylogeographic differentiation among avian populations and the evolutionary significance of subspecies. The Auk 109:626–36. Ballard JWO, Whitlock MC. (2004). The incomplete natural history of mitochondria. Mol Ecol 13:729–44. Birstein VJ, Ruban G, Ludwig A, Doukakis P, DeSalle R. (2005). The enigmatic Caspian Sea Russian sturgeon: How many cryptic forms does it contain? Syst Biodiv 3:203–18. Briggs JC. (2003). The biogeographic and tectonic history of India. J Biogeography 30:381–8. Brito PH. (2007). Contrasting patterns of mitochondrial and microsatellite genetic structure among western European populations of Tawny Owls (Strix aluco). Mol Ecol 16:3423–37. Burg TM, Croxall JP. (2001). Global relationships among black-browed and grey-headed albatrosses: Analysis of population structure using mitochondrial DNA and microsatellites. Mol Ecol 10:2647–60. Clement M, Posada D, Crandall KA. (2000). TCS: A computer program to estimate gene genealogies. Mol Ecol 9:1657–9. Craul M, Chikhi L, Sousa V, Olivieri G, Rabesandratana A, Zimmermann E, Radespiel U. (2009). Influence of forest fragmentation on an endangered large-bodied lemur in northwestern Madagascar. Biol Cons 142:2861–71. Debnath S. (2011). Clarias batrachus, the medicinal fish: An excellent candidate for aquaculture and employment generation. Proceedings of International Conference on Asia Agriculture an Animal, 2–3 July 2011, Hong Kong. Excoffier L, Laval G, Schneider S. (2005). Arlequin ver. 3.0: An integrated software package for population genetic data analysis. Evol Bioinfo Online 1:47–50. Excoffier L, Smouse P, Quattro J. (1992). Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131:479–91. Fu YX. (1997). Statistical neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147:915–25. Gershoni M, Templeton AR, Mishmar D. (2009). Mitochondrial bioenergetics as a major motive force of speciation. BioEssays 31: 642–50. Goossens B, Chikhi L, Ancrenaz M, Lackman-Ancrenaz I, Andau P, Bruford MW. (2006). Genetic signature of anthropogenic population collapse in orangutans. PLoS Biol 4:285–91. Hillis DM, Moritz C, Mable BK. (1996). Molecular systematics. 2nd ed. Sunderland, MA: Sinauer Associates, Inc. Hoelzel AR. (1998). Molecular genetic analysis of populations: A practical approach. Oxford: Oxford University Press. Hooper DU, Chapin FS, Ewel JJ, Hector A, Inchausti P, Lavorel S, Lawton JH, et al. (2005). Effects of biodiversity on ecosystem functioning: A consensus of current knowledge. Eco Mono 75:3–35. Hossain Q, Hossain MA, Parween S. (2006). Artificial breeding and nursery practices of Clarias batrachus (Linnaeus, 1758). Sci World 4: 32–7. IUCN (International Union for Conservation of Nature). (2007). Red List of Threatened Species, 2007. Available at: http://www.iucnredlist.org/ Khedkar GD, Reddy AC, Mann P, Ravinder K, Muzumdar K. (2009). C. batrachus (Linn.1758) population is lacking genetic diversity in India. Mol Biol Report 37:1355–62.


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Khedkar GD, Tiknaik AD, Shinde RN, Kalyankar AD, Ron TB, Haymer D. (2014a). High rates of substitution of the native catfish Clarias batrachus by Clarias gariepinus in India. Mitochondrial DNA. [Epub ahead of print]. doi:10.3109/19401736.2014.905863. Khedkar GD, Lutzky S, Rathod S, Kalyankar A, David L. (2014b). A dual role of dams in fragmentation and support of fish diversity across the Godavari River basin in India. Ecohydrology. [Epub ahead of print]. Kocher TD, Thomas WK, Meyers A, Edwards SV, Paabo S, Villablanca FX, Wilson AC. (1989). Dynamics of mitochondrial DNA evolution in animals: amplification and sequencing with conserved primers. Proc Natl Acad Sci USA 86:6196–200. Linnaeus C. (1758). Systema naturae per regna tria naturae, secundum classes, ordines, genera, species, cum characteribus, differentiis, synonymis, locis. Tomus I. Editio decima, reformata. Homiae: Laurentii Salvii. Ludwig A, Lippold S, Debus L, Reinartz R. (2009). First evidence of hybridization between endangered sterlets (Acipenser ruthenus) and exotic Siberian sturgeons (Acipenser baerii) in the Danube River. Biol Invasions 11:753–60. Lynch M, Crease TJ. (1990). The analysis of population survey data on DNA sequence variation. Mol Biol Evol 7:377–94. McCormack JE, Bowen BS, Smith TB. (2008). Integrating paleoecology and genetics of bird populations in two sky island archipelagos. BMC Biol 6:28. Nei M. (1987). Molecular evolutionary genetics. New York: Columbia University Press. Nei M, Kumar S. (2000). Molecular evolution and phylogenetics. New York: Oxford University Press. Olivieri GL, Sousa V, Chikhi L, Radespiel U. (2008). From genetic diversity and structure to conservation: Genetic signature of recent population declines in three mouse lemur species (Microcebus spp.). Biol Cons 141:1257–71. Posada D, Crandall KA. (2001). Evaluation of methods for detecting recombination from DNA sequences: Computer simulations. PNAS 98: 13757–62. Quemere E, Louis E, Riberon A, Chikhi L, Crouau-Roy B. (2009). Non-invasive conservation genetics of the critically endangered golden-crowned sifaka (Propithecus tattersalli): High diversity and significant genetic differentiation over a small range. Cons Genet 11: 675–87. Rogers AR, Harpending H. (1992). Population growth makes waves in the distribution of pairwise genetic differences. Mol Bio Evol 9: 552–69. Rozas J, Sanchez-DelBarrio JC, Messeguer X, Rozas R. (2003). DnaSP, DNA polymorphism analyses by the coalescent and other methods. Bioinformatics 19:2496–7. Slatkin M, Hudson RR. (1991). Pairwise comparisons of mitochondrial DNA sequences in stable and exponentially growing populations. Genetics 129:555–62. Smith TB, Wayne RK (Eds). (1996). Molecular genetic approaches in conservation. New York: Oxford University Press. Sousa V, Penha F, Pala I, Chikhi L, Coelho M. (2009). Conservation genetics of a critically endangered Iberian minnow: Evidence of population decline and extirpations. Anim Conserv 13: 162–71. Stephenson R, Kenchington E. (2000). Conserving fish stock structure is a critical aspect of preserving biodiversity. ICES CM Mini 7:7. Swenson NG, Howard DJ. (2004). Do suture zones exist? Evolution 58: 2391–7. Tajima F. (1989). Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123:585–95. Thakur NK. (1996). A biological profile of African catfish Clarias gariepinus and impact of its introduction in Asia. Proceedings of Fish Genetics and Biodiversity Conservation. NATCON publication 05: 315–22.