Genetic population structure of mud crab (Scylla olivacea) in the Bay of Bengal inferred from mitochondrial DNA and microsatellite markers

doi:10.21203/rs.3.rs-1999475/v1

The increasing exploitation of Scylla olivacea, one of the most commercially consumed mud crabs inhabiting mangroves and estuaries of the Bay of Bengal has been seriously experiencing a constant decline of its population. Information on its genetic population structure is crucial for the conservation of S. olivacea. Here, partial sequences of the mitochondrial cytochrome c oxidase subunit I (COI) gene and ten microsatellite loci were used to estimate population genetics from four localities in the Bay of Bangladesh. Genotypes of mitochondrial DNA (mtDNA) were identified with an 848 bp fragment sequence of COI gene, defining 32 different haplotypes from H1 to H32. H4 was the most frequently observed haplotype, existing in all four localities, while the majority of haplotypes were rare ones, existing in only one individual. Neutrality tests and mismatch analysis conferred a potential population expansion event for this crab species. Diploid genotypes were determined with microsatellite loci, delineating several alleles at each locus, significant linkage disequilibrium and deviation from Hardy-Weinberg equilibrium. The AMOVA analysis of mtDNA suggested a low level of genetic differentiation among four localities (FST = 0.0172, P > 0.05) while microsatellite markers showed a moderate genetic differentiation (FST = 0.0687, P > 0.05). The lower level of differentiation of mtDNA than microsatellites possibly resulted from female-mediated gene flow between populations, biased sex ratios, natural selection, or mutation.

Mitochondrial DNA

Microsatellite

Population structure

Scylla serrata

Bay of Bengal

Current biological and historical factors define the genetic architecture of natural populations. The variability of biparentally and uniparentally inherited genes is persuaded by these factors (Chesser & Baker, 1996), thereby, these two kinds of markers in estimation of population structure may confer valuable information concerning ecological and evolutionary features of natural population (Slatkin, 1994). Dispersal or conjugation with ecological or demographic intuition data derived from assessment of marker-specific structuring may be useful to formulate appropriate conservation strategies (Moritz, 1994, Petit et al., 2001). Little intraspecific genetic structuring is expected from marine organisms even over large geographic distance because of its few obvious barriers that allow high dispersal across wide geographical areas (Ward et al., 1994, Zardoya et al., 2004, McCusker & Bentzen, 2010). In addition, the passive transport of planktonic eggs and larvae of many marine species by ocean current implies high dispersal potential and ‘open’ populations in the marine environment (McCusker & Bentzen, 2010, Palumbi, 1992), hence resulting in low levels of genetic subdivision, finally heading to panmictic populations (McQuinn, 1997, Zardoya et al., 2004). Recently, however, paradoxical patterns have confronted such observations where species with long planktonic stages showing limited gene flow even over very short geographical distance (Wirth & Bernatchez, 2001, Olsen et al., 2008), revealing cryptic population structuring (Knutsen et al., 2003, Zardoya et al., 2004, Jirapunpipat et al., 2009). Thereby the genetic structure of natural populations of marine invertebrates cannot be reliably inferred from their apparent dispersal capacities.

The orange mud crab Scylla olivacea Herbst, 1796 (Decapoda: Portunidae), a large swimming shellfish is widely distributed over vast geographic areas ranging from southeastern and eastern Africa to Southeast Asia and Indo-Pacific regions and commonly found in mangrove forests and river mouths in estuarine environments (Fuseya & Watanabe, 1996, Klinbunga et al., 2000, Jirapunpipat et al., 2009). This species are of high demand in Asia because of its excellent and sought-after delicacy and females with mature ovaries are particularly expensive (Knuckey, 1996). This portunid is popularly used for aquaculture because of its hardy nature and ease of capture. Recently, mud crab farming has gained much attention in Bangladesh. However, most of these farming activities involve merely crab fattening and the production of soft-shelled crabs, with the use of captured juveniles from the wild (Begum, 2009, Ferdoushi & Xlang–guo, 2010). As a lucrative commercial fishery, this shellfish has experienced population declines because of the degradation of natural habitats, increasing of water pollution, and overexploitation. In spite of its commercial interest and the declining status of populations, globally, knowledge of the genetics population structure of wild populations is limited to a few studies (Gopurenko, 1999, 2002a, b, Keenan, 1998, Xu, 2009). In Bangladesh, some studies accomplished were based on only culture (Begum, 2009, Ferdoushi & Xlang–guo, 2012), breeding (Sarower et al., 2012), biochemical composition (Sarower et al., 2013), taxonomic confirmation (Rouf et al., 2016, Sarower et al., 2017,), morphometric analysis (Rouf et al., 2016, Asaduzzaman et al., 2020), marketing (Ferdoushi et al., 2010, Istiak, 2018), mud crab fishery (Rahman et al., 2020), peak breeding season identification (Ali et al, 2020) but no report or research hitherto has been exposed yet to assess population dynamics, intraspecific patterns of genetic variability and the population structure of S. olivacea using molecular genetic markers.

The aim of this research was to determine if there are genetically distinct populations of S. olivacea in the Bay of Bengal. Using mtDNA gene sequences and microsatellite DNA markers, we examined the genetic diversity and population differentiation of S. olivacea collected at four locations in the Bay. This study increases current knowledge of mud crab biology by providing important management information by determining whether multiple stocks exist throughout the studied regions.

Mud crabs sample collection and its DNA extraction

A total of 87 individuals of S. olivacea were collected from four different localities in the Bay of Bengal: Satkhira (SAT, N. 23), Khulna (KHU, N. 20), Bagerhat (BAG, N. 21), and Cox’s Bazar (COX, N. 23) (Figure 1). From each specimen, muscle tissue was removed from a single claw and stored at -80 ^oC prior to DNA extraction. Genomic DNA was extracted from muscle tissue using DNAZOL® Reagent (Invitrogen Life Technologies, USA) by following manufacture’s protocols and stored at -20 ^oC until being used.

PCR amplification and sequencing of COI gene

An 848-base pair (bp) segment of the mitochondrial cytochrome oxidase c subunit I (COI) gene was amplified by polymerase chain reaction (PCR) using two specific primers designed from the alignment sequences of insect and Scylla spp.: MTD_COIF 5’-AAT ACA TCC TTT TTT GAT CCT GC-3 and MTD_COIR 5’-TAA GAG TGG TTT CAT TCA ATT GA-3’ corresponding to 2112 and 2961 locations of the Drosophila yakuba mitochondrial COI gene sequence, respectively (Clary & Wolstenholme, 1985). PCRs were performed in 25 ml total volume that included 0.4 mM each primer, 0.2 mM each dNTP, 10X PCR buffer 0.75 unit Blend Taq Plus DNA polymerase (Toyobo Co. Ltd., Osaka, Japan), and approximately 100 ng template DNA under the following conditions: one cycle of denaturation at 94 ^oC for 5 min, 35 cycles of 0.5 min at 94 ^oC, 0.5 min at a primer-specific annealing temperature 53 °C, and 1 min of extension at 72 ^oC , using a Bio-Rad PCR system T100 thermal cycler (Bio-Rad Laboratories, Hercules, CA, USA). As a final step, products were extended for 10 min at 72^oC. PCR products were separated by agarose gel electrophoresis, purified by means of magnetic silica beads of MagExtractor kit (Toyobo Co. Ltd.) and directly sequenced using the primers of MTD_COIF and MTD_COIFR on ABI 3730XL sequencing machines (Applied Biosystems, Foster City, CA, USA).

Microsatellite genotyping

Ten microsatellite loci developed and characterized previously from S. serrata (Gopurenko, 2002a) and S. paramamosain (Ma et al., 2010) followed by evaluation with S. olivacea were successfully amplified by polymerase chain reaction (PCR) in preliminary trials (Table 1). Each sample from the same 87 individuals of S. olivacea was genotyped using these 10 microsatellite markers. PCRs were performed as aforementioned conditions exceptionally using a primer-specific annealing temperature (Table 1). The PCR products were subjected to electrophoresis through a quartz microchip electrophoresis system on a next generation MCE-202 MultiNA (Shimadzu Corporation, Kyoto, Japan). A 25 bp DNA marker (Invitrogen Life Technologies) was used to estimate the sizes of microsatellite alleles.

Data analysis

Sequences were edited using software BioEdit ver 7.0.1 (Hall, 1999) and manually revised. The MEGA 4.0 software (Tamura et al., 2007) was used to identify the variable and parsimonious sites. Dna SP version 4.1 (Rozas, 2003) software was used to identify haplotypes and then they were deposited into GenBank database (the accession numbers are listed in Table 2). A median-joining network of phylogenetic relationships among haplotypes was developed using software Network version 4.51 (Polzin & Daneshmand, 2003). Software Arlequin version 3.11 (Excoffier, 2005) was employed to calculate haplotype diversity (h) and nucleotide diversity (p) according to Nei (1987) for each population and overall population. The index h indicates the probability that two randomly chosen haplotypes were different in a single population, and p shows mean number of differences between all pairs of haplotypes in a single population. Neutrality tests of Ewens-Watterson (Ewens, 1972, Watterson, 1978), Chakraborty (1990), Tajima D (Tajima, 1989), and Fu’s F_S (Fu, 1997) and mismatch analysis (Rogers, 1995) were carried out using Arlequin 3.11 software. By calculating the number of alleles (N_A), expected heterozygosity (H_E), observed heterozygosity (H_O) and other relevant measures, genetic diversity for each locus and each population, as well as overall loci and populations, deviations from Hardy–Weinberg equilibrium (HWE) and linkage disequilibrium were quantified using Arlequin. Furthermore, analysis of molecular variance (AMOVA), implemented in Arlequin (Excoffier et al., 1992) and the fixation index F_ST value were performed to explain the genetic differentiation between and within populations. The significant level of the test was measured using 1000 permutations of each pairwise comparison.

Characteristics of partial COI sequence

An 848 bp length segment of mtDNA COI gene was sequenced from 87 individuals of S. olivacea. The average nucleotide frequencies of A, T, G, C among all sequences were 27.14%, 35.43%, 15.20% and 22.23%, respectively. The A/T contents (62.57%) were significantly higher than the G/C ones (37.43%). A total of 40 variable sites were identified, of which 16 were parsimonious sites and others were singleton sites, no insertion or deletion was found. Among the variable sites, 23 contained transitions, one contained transversion, and one substitution resulted in amino acid changes. The details about variable sites of COI sequence are shown in Table 2.

Characteristics of haplotypes and genetic diversity of S. olivacea

Among the total 87 sequences, 32 different haplotypes were classified, of which highest number of haplotype were from BAG followed by COX and SAT. H4 was the most frequent haplotype (23.0% of all individuals) found in all four localities followed by H2 (11.5%), H17 (8%), and H8 (5.8%). Nine haplotypes were found to be present in two to four individuals, but the majority of haplotypes (19 halotypes) were rare ones present in only one individuals. The average h of all localities was 0.900, ranging from 0.805 (KHU) to 0.976 (BAG). The average p was 0.006, ranging from 0.005 (KHU) to 0.007 (BAG) (Table 3).

Genetic differentiation and phylogeny of S. olivacea

Pairwise genetic distances ranged from 0.0049 to 0.0070 within each locality and from 0.0049 to 0.0065 between localities (Table 4). AMOVA suggested that only 1.7% of the total genetic variation was contributed by among populations (F_ST = 0.0172, P > 0.05) whereas 98.3% of the variation was devoted by within population (Table 5). No significant genetic differentiation was observed among four localities except between COX and KHU (F_ST = 0.0824, P < 0.05), and between COX and SAT (F_ST = 0.0574, P < 0.05). All of these analyses indicated a low level of genetic differentiation among four localities, suggesting a limited genetic structure of S. olivacea in the Bay of Bengal. To examine the genealogy of COI haplotypes, a median-joining network was built. The topology of this network (Figure 2) displays two major clades (haplogroups 1 and 2) separated by long-branch lengths, each containing common haplotypes. In addition, this topology with a high ratio of unique haplotype (59.4%) was observed (Figure 2), implying a historical population expansion event. The haplotypes H4 and H2 are present in the centre of haplogroup 1 and 2, respectively, and are closely related to the majority of the haplotypes of two clades, suggesting these were the ancestral haplotypes.

Neutrality tests and mismatch distribution analysis

All tests of neutral evolution except Ewens-Watterson (F_O = 0.089 and F_E = 0.063, P > 0.05) provided very congruent results, revealing significant deviations from mutation–drift equilibrium for the pooled sample (Table 6). In Tajima D and Fu’s F_S tests, the negative values were detected in all localities except KHU, with the total values of -1.218 and -14.119, respectively. These statistical findings implied that S. olivacea may undergo population selection or expansion events. The demographic features of mismatch distribution for each location are shown in Table 7. The estimated effective population size after population growth was significantly larger than that before population growth for each location (P < 0.01).

The sum of the square deviations (SSD) per locality ranged from 0.00361 to 0.09395, with an average of 0.03321. The statistical non-significance (P > 0.05) of SSD between the observed and expected mismatch distributions revealed the presence of non-equilibrium and a population expansion event for S. olivacea. Moreover, the raggedness indices (Rag) per locality were between 0.00764 and 0.21947, with an average of 0.47250 (P > 0.05), also providing evidence for population expansion.

Microsatellite allelic diversity and Hardy-Weinberg equilibrium

Genotypes of S. olivacea were determined for 10 microsatellite loci. Polymorphism varied between microsatellite loci, ranging from two (Scpa04 and Scpa09) to twenty four (Ss-101) alleles. Total number of alleles for each locus ranged from 3 to 33 (Table 8). Number of individuals analyzed (N_S), number of alleles (N_A), inbreeding co-efficient (F_IS), observed heterozygosity (H_O), expected heterozygosity (H_E), and deviation from Hardy–Weinberg expectations (P) for each locus within each sample site are provided in Table 8. Significant linkage disequilibrium (p = 0.05) was found in 33 out of 180 pairwise comparisons among 10 loci for all populations. However, most of the genotypic linkage disequilibrium detected was in SAT population. Hence, about 18% of the markers were considered to be independently segregating.

The mean observed heterozygosity for each population ranged from 0.010 to 0.896 and mean expected heterozygosity from 0.442 to 0.965 (Table 8). Overall, there were significant heterozygote deficiencies for most of the samples in all locations, but heterozygote excess (i.e., a negative F_IS value) was found for five loci, Scpa01 in SAT and KHU, Scpa09 in SAT and BAG, Ss-101 in SAT and KHU, Ss-403 in COX and Ss-513 in SAT and BAG location. Thirty one of the 40 single locus tests for deviation from HWE locations were significant at P <0.05, P <0.01, and P <0.001. Significant deviation from HWE was resulted from excess of homogygotes (positive F_IS value) and excess of hetrozygotes (negative F_IS value).

Population structure

Overall, most genetic variation at these microsatellite loci for S. olivacea was found in among individuals within the population (Table 9). From our AMOVA using the distribution of different alleles (i.e. an F_ST measure), 59.72% of all variation in S. olivacea was observed within individuals. Most of the remaining significant genetic variation resided among individuals within population (33.42%), and only a small fraction (6.87%) can be attributed to differences among population. The F_ST value (0.0687) indicated moderate genetic differentiation within the analyzed population with sensible gene flow.

Historical events (e.g., bottlenecks, range expansion, zone of admixture etc.) and contemporary gene flow play role to shape the genetic variability of populations (Pierce et al., 2017). As ecological and environmental factors have the relative influence of these phenomena, different portion of the range of a single species will likely make different genetic architecture (Weiss et al., 2022). The aim of this study was to extend knowledge on the genetic population structure and evolutionary history of S. olivacea, an important resource for South and Southeast Asian fishery. Thus we present here results describing the patterns of genetic differentiation and dispersal strategies using both maternally-inherited COI and microsatellites markers. Although S. olivacea is still a relatively wide spread species, the recent overexploitation of these crabs has suffered natural stocks decline in the wild (Le Vay, 2001). We particularly focused on populations in the Bay of Bengal, an area where this species are widely exploited and which might shape the genetic variation.

The dispersal ability (Hunt, 1993, Bohonak, 1999, Collin, 2002) and oceanic landscapes, especially the marine currents (Baus, 2005, Kenchington, 2006, Zhan et al., 2009, Ni et al., 2011) are the most common hypotheses elucidating the genetic connectivity and differentiation among different geographical populations of marine organisms. In this study, high levels of genetic variability within populations and low level of genetic differentiation among populations were derived from COI and microsatellite markers, revealing strong genetic connectivity on this spatial scale. The results of our study were generally consistent with the results of the genetic diversity and population structure analyses of S. paramamosain in Hainan Island and mainland coasts of China, which shows no genetic differentiation among samples probably due to the adult and juvenile migrations between ocean basins and adjacent continental margins, high dispersal capabilities of larvae, and limits of physical barriers in the marine environment (He et al., 2010, Ma et al., 2011). Moreover, several other sea animals also showed low genetic differentiation among different geographic populations, such as shrimps (Penaeus chinensis and Feneropenaeus chinensis) (Liu et al., 2004, Li et al., 2009, Zhang et al., 2010), yellow drum (Nibea albiflora) (Han et al., 2008) and mollusk (Atrina pectinata) (An et al., 2012). The results were not in agreement with that of other mud crabs (S. paramamosain and S. serrata) (Fratini & Vannini, 2002, Takano et al., 2005, Ma, 2010) which showed significant population genetic structure in the same region. Further, the high genetic variation also has been observed in other sea animals, such as scallop (Chlamys farreri) (Zhao & Kong, 2009) and Atlantic salmon (Salmo salar) (Karlsson et al., 2010), blood clam (Scapharca broughtonii) (Cho, 2007), duck clam (Mactra veneriformis) (Zhang, 2008), Chinese scallop (Chlamys farreri) (Zhan, 2009), and bivalve mollusk (Mactra chinensis) (Ni & Kong, 2011). The life history characteristics, environmental heterogeneity and large population sizes may help to maintain a high level of genetic diversity (Nei, 1987, Avise, 1998). Two biological characteristics of this portunid crab might be accountable for the high gene flow among spatial samples. First, for species with a planktonic larval stage that lasts for 3-4 weeks (Brick, 1974, Ong, 1964) is assumed to influence the scale of connectivity (Selkoe & Toonen, 2011). The longer planktonic life of this portunid crab might overcome barriers caused by the complex hydrology and bathymetry in the Bay of Bengal, which may result in high gene flow among geographical populations as previously described for other marine species with planktonic larvae (Palumbi, 1992). Second, marine distribution patterns of this portunid from inshore to offshore during spawning. The portunid crab is shown to migrate offshore to spawn that leads to be transported by the coastal current in longer distance than in other species (Hill, 1994).

The negative and significant Tajima’s D and Fu’s Fs values, a star-like shape of minimum spanning network, non-significance of SSD between the observed and expected mismatch distribution, and the raggedness index derived from COI data validate population expansion of S. olivacea. Nevertheless, the same molecular signature as population demographic expansion can be acquired from population range expansion, even if the migration rate between local populations is soaring (Excoffier, 2003, Liu et al., 2006). The genetic differentiation among populations for nuclear microsatellite data was relatively high (F_ST = 0.0687, P = 0.001) compared to genetic differentiation for mitochondrial DNA (F_ST = 0.0172), which indicated relatively high female gene flow among populations, skewed sex ratios, natural selection, or mutation. However, overall low genetic differentiation among different localities of S. olivacea inferred from AMOVA analysis, suggesting a limited genetic structure. Our results revealed that the weak level of genetic structure and strong connectivity of S. olivacea was probably results from both the gene flow (through the larval dispersal) and the lack of isolation-by-distance.

The results of this study suggested that, despite high level of genetic diversity within local populations of S. olivacea in the Bay of Bengal, there are no significant genetic differentiations among samples. Therefore, S. olivacea distributed in the Bay of Bengal could be managed as a single unit. The information about the magnitude and pattern of genetic diversity elucidated in this study can be useful for assessment of stocks resource, sustainably exploitation and conservation, and selective breeding of this mud crab species.

Funding

MG Sarower thanks the Japan Society for the Promotion of Science (JSPS) for Invitation Research Fellowship. This work was partly supported by a Grant-in-Aid for Scientific Research (JSPS/OFF214/085) from the same society in Japan.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [full name], [full name] and [full name]. The first draft of the manuscript was written by [full name] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

ACKNOWLEDGMENTS

The authors would like to thank all the many people who helped in the collection of the mud crab samples particularly Satchidananda Biswas Satu, Assistant Director, Shushilan, Bangladesh. We thank Partho Pratim Debnath, WorldFish Bangladesh and Sheik Shahriar for help with DNA extractions and Hiromasa Nakamura for technical assistance in DNA microchip electrophoresis analyses. MG Sarower was supported by the Invitation Fellowships for Research in Japan from Japan Society for the Promotion of Science.

Ali MY, Hossain MB, Sana S, Rouf MA Yasmin S and Sarower MG (2020) Identifying peak breeding season and estimating size at first maturity of mud crab (Scylla olivacea) from a coastal region of Bangladesh. Heliyon 6, e04318.
An HS, Lee JW and Dong CM (2012) Population genetic structure of Korean pen shell (Atrina pectinata) in Korea inferred from microsatellite marker analysis. Genes and Genomics 34, 681–688.
Asaduzzaman M, Noor AR, Islam MM and Rahman MM (2020) Multivariate morphometric investigation to delineate species diversity and stock structure of mud crab Scylla sp. along the coastal regions of Bangladesh. Aquaculture and Fisheries. Published online: 03 April 2020.
Avise J (1998) Phylogeography. Cambridge: Harvard University Press
Baus E, Darrock D and Bruford M (2005) Gene-flow patterns in Atlantic and Mediterranean populations of the Lusitanian sea star Asterina gibbosa. Molecular Ecology 14, 3373–3382,
Begum M, Shah MMR, Mamun AA and Alam MJ (2009) Comparative study of mud crab (Scylla serrata) fattening practices between two different systems in Bangladesh. Journal of the Bangladesh Agricultural University 7, 151–156.
Bohonak AJ (1999) Dispersal, gene flow, and population structure. The Quarterly Review of Biology 74, 21–45.
Brick RW (1974) Effects of water quality, antibiotics, phytoplankton and food on survival and development of larvae of Scylla serrata. Aquaculture 3, 231–244.
Chakraborty R. (1990) Mitochondrial DNA polymorphism reveals hidden heterogeneity within some Asian populations. American Journal of Human Genetics 47, 87–94.
Chesser R.K. and Baker R.J. (1996) Effective sizes and dynamics of uniparentally and diparentally inherited genes. Genetics 144, 1225–1235.
Cho ES, Jung CG, Sohn SG, Kim CW and Han SJ (2007) Population genetic structure of the ark shell Scapharca broughtonii Schrenck from Korea, China, and Russia based on COI gene sequences. Marine Biotechnology 9, 203–216.
Clary DO and Wolstenholme DR (1985) The mitochondrial DNA molecule of Drosophila yakuba: nucleotidesequence, gene organisation and genetic code. Journal of Molecular Evolution 22, 252–271.
Collin R (2002) The effects of mode of development on phylogeography and population structure of North Atlantic Crepidula (Gastropoda: Calyptraeidae). Molecular Ecology 10, 2249–2262.
Ewens WJ (1972) The sampling theory of selectively neutral alleles. Theoretical Population Biology 3, 87–112.
Excoffier L (2003) Patterns of DNA sequence diversity and genetic structure after a range expansion: lessons from the infinite-island model. Molecular Ecology 13, 853–64.
Excoffier L, Laval G and Schneider S (2005) Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online 1, 47–50.
Excoffier L, Smouse PE and Quattro JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131, 491–497.
Ferdoushi Z, Zhang X, And Hasan RM (2010). Mud crab (Scylla sp.) marketing system in Bangladesh, Asian Journal of Food and Agro-Industry 3, 248–265.
Ferdoushi Z and Xiang-Guo Z (2010) Role of women in mud crab (Scylla sp.) Fattening in the south-west part of Bangladesh. Marine Research and Aquaculture 1, 5–13.
Fratini S and Vannini M (2002) Genetic differentiation in the mud crab Scylla serrata (Decapoda: Portunidae) within the Indian Ocean. Journal of Experimental Marine Biology and Ecology 272, 103–116.
Fu YX (1997) Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147, 915–925.
Fuseya R and Watanabe S (1996) Genetic variability in the mud crab genus Scylla (Brachyura: Portunidae). Fisheries Science 62, 705–709.
Gopurenko D and Hughes JM (2002b) Regional patterns of genetic structure among Australian populations of the mud crab Scylla serrata (Crustecea: Decapoda): evidence from mitochondrial DNA. Marine and Freshwater Research 53, 849–857.
Gopurenko D, Hughes JM and Keenan CP (1999) Mitochondrial DNA evidence for rapid colonization of the Indo-West Pacific by the mud crab Scylla serrata. Marine Biology 134, 227–233.
Gopurenko D, Hughes JM and Ma J (2002a) Identification of polymorphic microsatellite loci in the mud crab Scylla serrata (Brachyura: Portunidae). Molecular Ecology Notes 2, 481–483.
Hall T (1999) BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symposium Series 41, 95–98.
Han ZQ, Gao TX, Yanagimoto T and Sakurai Y (2008) Genetic population structure of Nibea albiflora in the Yellow Sea and East China sea. Fisheries Science 74, 544–552.
He L, Zhang A, Weese D, Zhu C, Jiang C and Qiao Z (2010) Late Pleistocene population expansion of Scylla paramamosain along the coast of China: a population dynamic response to the Last Interglacial sea level high stand. Journal of Experimental Marine Biology and Ecology 385, 20–28.
Hijmans RJL, Guarino C, Bussink P, Mathur M, Cruz I, Barrentes and Rojas E (2004) DIVA-GIS. Vsn. 5.0. A geographic information system for the analysis of species distribution data. Manual available at http://www.diva-gis.org
Hill BJ (1994) Offshore spawning by the portunid crab Scylla serrata (Crustacea: Decapoda). Marine Biology 120, 379–384.
Hunt A (1993) Effects of contrasting patterns of larval dispersal on the genetic connectedness of local populations of two intertidal starfish, Patiriella calcar and P. exigua. Marine Ecology Progress Series 92, 179–186.
Istiak SM (2018) Study for assessing mud crab (Scylla serrata, Forskal, 1755) market chain and value-added products development in Bangladesh. Bangladesh Journal of Zoology 46, 263 – 273.
Jirapunpipat K, Yokota M and Watanabe S (2009) The benefits of species-based management of sympatric mud crabs migrating to a common fishing ground. ICES Journal of Marine Science 66, 470–477.
Karlsson S, Moen T and Hindar K (2010) Contrasting patterns of gene diversity between microsatellites and mitochondrial SNPs in farm and wild Atlantic salmon. Conservation Genetics 11, 571–582.
Keenan CP, Davie PJF and Mann DL (1998) A revision of the genus Scylla De Haan, 1833 (Crustacea: Decapoda: Brachyura: Portunidae). Raffles Bulletin of Zoology 46, 217–245.
Kenchington E, Patwary M, Zouros E and Bird C (2006) Genetic differentiation in relation to marine landscape in a broadcast-spawning bivalve mollusc (Placopecten magellanicus). Molecular Ecology 15, 1781–1796.
Klinbunga S, Boonyapakdee A and Pratoomchat B. (2000) Genetic diversity and species-diagnostic markers of mud crabs (Genus Scylla) in Eastern Thailand determined by RAPD analysis. Marine Biotechnology 2,180–187.
Knuckey IA (1996) Maturity in male mud crabs, Scylla serrata and the use of mating scars as a functional indicator. Journal of Crustacean Biology 16, 487–495.
Knutsen H, Jorde PE, Andre´ C and Stenseth NC (2003) Fine-scaled geographical population structuring in a highly mobile marine species: the Atlantic cod. Molecular Ecology 12, 385–394.
Le Vay L (2001) Ecology and management of mud crab Scylla spp. Asian Fisheries Science 14, 101–111.
Li YL, Kong XY, Yu ZN, Kong J, Ma S and Chen LM (2009) Genetic diversity and historical demography of Chinese shrimp Feneropenaeus chinensis in Yellow Sea and Bohai Sea based on mitochondrial DNA analysis. African Journal of Biotechnology 8, 1193–1202.
Liu JX, Gao TX, Zhuang ZM, Jin XS, Yokogawa K and Zhang YP (2006) Late Pleistocene divergence and subsequent population expansion of two closely related fish species, Japanese anchovy (Engraulis japonicus) and Australian anchovy (Engraulis australis). Molecular Phylogenetics and Evolution 40, 712–723.
Liu P, Meng XH, Kong J, Zhuang ZM, Ma CY and Wang QY (2004) Genetic diversity analysis of Penaeus chinensis by microsatellite DNA. Proceedings of National Science 14, 333–338.
Ma H, Ma C and Ma L (2011) Population genetic diversity of mud crab (Scylla paramamosain) in Hainan Island of China based on mitochondrial DNA. Biochemical Systematics and Ecology 39, 434–440.
Ma HY, Ma CY, Ma LB and Cui HY (2010) Novel polymorphic microsatellite markers in Scylla paramamosain and cross-species amplification in related crab species. Journal of Crustacean Biology 30, 441–444.
Ma HY, Ma CY, Ma LB and Cui HY (2010) Novel polymorphic microsatellite markers in Scylla paramamosain and cross-species amplification in related crab species. Journal of Crustacean Biology 30, 441–444,
McCusker MR and Bentzen P (2010) Positive relationship between genetic diversity and abundance in fishes. Molecular Ecology 19, 4852–4862.
McQuinn IH (1997) Metapopulations and the Atlantic herring. Reviews in Fish Biology and Fisheries 7, 297–329.
Moritz C (1994) Defining ‘evolutionary significant units’ for conservation. Trends in Ecology & Evolution 9, 373–375.
Nei M (1987) Molecular Evolutionary Genetics. New York: Columbia University Press,
Ni L, Li Q and Kong L (2011) Microsatellites reveal fine-scale genetic structure of the Chinese surf clam Mactra chinensis (Mollusca, Bivalvia, Mactridae) in Northern China. Marine Ecology 32, 488–497.
Nielsen SE, Herrero S, Boyce MS, Mace RD, Benn B, Gibeau ML and Jevons S (2004) Modelling the spatial distribution of humancaused grizzly bear mortalities in the Central Rockies ecosystem of Canada. Biological Conservation 120, 101–113.
Olsen SM, Hansen B, Quadfasel D and Østerhus S (2008) Observed and modeled stability of overflow across the Greenland–Scotland ridge. Nature 455, 519–523.
Ong KS (1964) Early developmental stages Scylla serrata (Crustacea Portunidae), reared in the laboratory. Proceedings Indo- Pacific Fishery Commission 11, 135–146.
Palumbi SR (1992) Marine speciation on a small planet. Tree 7, 114–118.
Petit E, Balloux F and Goudet J (2001) Sex-biased dispersal in a migratory bat: a characterization using sex-specific demographic parameters. Evolution 53, 635–640.
Pierce AA, Gutierrez R, Rice, AM, & Pfennig, KS. (2017). Genetic variation during range expansion: effects of habitat novelty and hybridization. Proceedings of the Royal Society B: Biological Sciences, 284(1852), 20170007.
Polzin T and Daneshmand SV (2003) On steiner trees and minimum spanning trees in hypergraphs. Operations Research Letters 31, 12–20.
Rahman MM, Haque SM, Galib SM, Islam MA, Parvez MT, Hoque MN, Wahab MA, Egna H and Brown C (2020) Mud crab fishery in climate vulnerable coastal Bangladesh: an analysis towards sustainable development. Aquaculture International 28, 1243–1268.
Rogers A (1995) Genetic evidence for a pleistocene population explosion. Evolution 49, 608-615.
Rouf MA, Shahriar SIM, Sarower MG, Ahsan MN (2016) Taxonomic clarification of mud crab species of genus Scylla (Brachyura: Portunidae) available in the coastal regions of Bangladesh. Asian Fisheries Science 29, 124–136.
Rozas J, Sanchez-Delbarrio JC, Messeguer X and Rozas R (2003) DnaSP, DNA polymorphism analyses by coalescent and other methods. Bioinformatics 19, 2496–2497.
Sarower MG, Aktar N, Mostafa M, Sabbir W and Islam MS (2012) Some aspects of captive breeding biology of mud crab, Scylla serrate in Bangladesh. Journal of Innovation and Development Strategy 6, 1–6.
Sarower MG, Bilkis S, Rauf A, Khanam M and Islam S (2013) Comparative Biochemical Composition of Natural and Fattened Mud Crab Scylla serrata. Journal of Scientific Research 5, 545–553.
Sarower MG, Shahriar SIM, Nakamura H, Rouf MA, Okada S. (2017) Taxonomic confirmation of mud crab species (genus Scylla) in Bangladesh by nuclear and mitochondrial DNA markers. Mitochondrial DNA Part A 28 (6), 935–940.
Selkoe K and Toonen RJ (2011) Marine connectivity: a new look at pelagic larval duration and genetic metrics of dispersal. Marine Ecology Progress Series 436, 291–305.
Slatkin M (1994). Gene flow and population structure. In Real L., (Eds) Ecological Genetics, Princeton, New Jersey: Princeton University Press, pp 3–17.
Tajima F (1989) Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595.
Takano M, Barinova A, Sugaya T, Obata Y, Watanabe T, Ikeda M and Taniguchi N (2005) Isolation and characterization of microsatellite DNA markers from mangrove crab, Scylla paramamosain. Molecular Ecology Notes 5, 794–795.
Tamura K, Dudley J, Nei M and Kumar S (2007) MEGA 4: molecular evolutionary genetics analysis (MEGA) software version 4.0. Molecular Biology and Evolution 24, 1596–1599.
Ward RD, Woodwark M and Skibinski DOF (1994) A comparison of genetic diversity levels in marine, freshwater and anadromous fish. Journal of Fish Biology 44, 213–232.
Watterson G (1978) The homozygosity test of neutrality. Genetics 88, 405–417.
Weiss, M, Weigand, H, & Leese, F. (2022). Individual small in‐stream barriers contribute little to strong local population genetic structure five strictly aquatic macroinvertebrate taxa. Ecology and evolution, 12(4), e8807.
Wirth T and Bernatchez L (2001) Genetic evidence against panmixia in the European eel. Nature 409, 1037–1040.
Xu JC, Grumbine R., Shrestha A, Eriksson M, Yang XF, Wang Y and Wilkes A (2009) The melting Himalayas: cascading effects of climate change on water, biodiversity and livelihoods. Conservation Biology 23, 520–530.
Zardoya R, Castilho R, Grande C, Favre-Krey L, Caetano S, Marcato S, Krey G and Patarnello T (2004) Differential population structuring of two closely related fish species, the mackerel (Scomber scombrus) and the chub mackerel (Scomber japonicus) in the Mediterranean Sea. Molecular Ecology 13, 1785–1798.
Zhan A, Hu J, Hu X, Zhou Z, Hui M, Wang S, Peng W, Wang M and Bao Z (2009) Fine-scale population genetic structure of Zhikong scallop (Chlamys farreri): Do local marine currents drive geographical differentiation? Marine Biotechnology 11, 223–235.
Zhang J (2008) The study of the molecular population genetic and phylogeny of Mactra veneriformis using 28SrRNA gene and ITS1. Master’s thesis: Liaoning Normal University, Liaoning Province, China (in Chinese with English abstracts).
Zhang T, Kong J, Wang W and Wang Q (2010) Genetic variability assessed by microsatellites in the breeding populations of the shrimp Penaeus (Fenneropenaeus) chinesis in in China. Aquaculture 310, 229–233.
Zhao C., Li Q. and Kong L. (2009) Inheritance of AFLP markers and their use for genetic diversity analysis in wild and farmed scallop (Chlamys farreri). Aquaculture 287, 67–74.

Table 1. Locus name, repeat motif, primer sequences, annealing temperature (T_a) and GeneBank accession numbers of the ten microsatellite markers used in this study. All loci and primers were characterized from S. paramamosain (Ma et al., 2010) and S. serrata (Gopurenko, 2002a)

Locus

Repeat motif

Primer sequences (5’ – 3’)

T_a (°C)

GenBank

accession no.

Scpa01

Scpa03

Scpa04

Scpa05

Scpa09

Ss-101

Ss-102

Ss-103

Ss-104

Ss-105

(AGT)10AA(TAG)4N10(AGTAGC)3(AGT)3

(TGTA)2N5(AT)4

(AG)6AA(AG)2AT(AG)46

(GT)18

(AACAC)6

(AG)36

(GA)36

(GA)37

(CT)24

(CT)14

F:CGATTAACCATGCCTTAAAATAA

R:ATCAGCTCAGGGCAAAACTGA

F:CTGTAACACCCCAAAACAT

R:GCCCAGGTACTCTCCACTC

F:CGGAAGTGTACTCGTAAA

R:GTTTTATCAATTGCCTTT

F:GTTCCGAACTTCCCCAACAC

R:TTGTGAAATGAGCGAAGATGAC

F:GCAACACAATATAATGCAATGTAA

R:AGCGTGACCTGCTGATAGG

F: ATTCAACACGCGCGCGTACGC

R: GCAGTTTACCATATGCTTGGG

F: GTTATATAAGAAATAATGTCC

R: GTTCCTGCTATGTAATCCCG

F: TCATTCTCAGTACCTTTAATC

R: GTTATCGTCTGCTGGGACC

F: GACAAAGGAGCACTCAGCCAC

R: GAAGGATTCACTTGTCCACGC

F: GGCCGGGTGAGGGATGAGCC

R: CGTTTCCGCAACCAACAGATG

54

52

46

56

53

55

45

55

GU182881

GU182883

GU182884

GU182885

GU182889

AF 508135

AF 508134

(AF 508133

AF 508132

AF 508131

Table 2. Polymorphic sites among 32 mitochondrial haplotypes of S. oilvacea in the Bay of Bengal (H1 to H32). Haplotypes are compared with consensus haplotype H1, “.” indicates identical nucleotides, “N” indicates number of haplotype in all localities, “AA1 and AA2” indicates amino acids translated by haplotype H1 and by other haplotypes, respectively after mutation.

Haplotype	Polymorphic nucleotide position																																								N	Genbank accession no.
	3	3	5	7	7	1	1	2	2	2	2	2	2	2	3	3	3	3	4	4	4	4	4	5	5	5	5	5	5	6	6	6	6	6	7	7	8	8	8	8
	1	6	7	2	5	5	9	0	2	2	3	3	5	9	0	0	7	9	0	1	2	4	5	0	0	0	5	6	6	0	0	2	3	6	1	9	0	1	2	2
						9	8	4	5	8	1	7	8	7	0	3	5	9	8	4	3	4	9	1	4	7	8	4	7	0	9	7	9	0	4	8	4	9	3	4
H1	A	C	A	G	T	C	T	G	T	T	A	A	C	C	C	G	A	C	T	T	C	C	T	C	A	A	A	G	C	T	C	A	A	T	C	T	A	T	T	C	1	KT964568
H2	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	C	.	.	.	.	.	.	.	.	.	.	.	.	.	G	.	.	.	.	.	.	.	10	KT964569
H3	.	.	.	.	.	.	C	.	.	.	G	.	A	.	.	.	G	.	.	.	.	T	.	.	.	.	.	A	.	.	.	.	G	.	.	.	.	C	.	.	4	KT964570
H4	.	.	.	.	.	.	.	.	.	.	G	.	A	.	.	.	.	.	C	.	.	.	.	.	.	.	.	A	.	.	.	G	G	.	.	.	.	.	.	.	20	KT964571
H5	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	T	.	.	.	.	.	.	.	.	G	.	.	.	.	.	.	.	2	KT964572
H6	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	G	C	.	C	.	.	.	.	1	KT964573
H7	.	.	.	A	.	.	.	.	.	.	G	.	A	.	.	.	.	.	C	.	.	.	.	.	.	.	.	A	.	.	.	G	G	.	.	.	.	.	.	.	1	KT964574
H8	.	.	.	.	.	T	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	G	.	.	.	.	.	.	.	5	KT964575
H9	.	.	.	A	.	T	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	G	.	.	.	.	.	.	.	1	KT964576
H10	.	.	.	.	.	.	.	.	.	.	G	.	A	.	.	.	.	.	C	.	.	.	.	.	.	.	.	A	.	.	.	.	G	.	.	.	.	.	.	.	1	KT964577
H11	.	.	.	.	.	.	.	.	.	.	G	.	A	.	.	.	.	.	C	.	.	.	.	.	.	.	.	A	A	.	.	G	G	.	.	.	.	.	.	.	1	KT964578
H12	.	.	.	.	.	.	.	.	.	.	G	.	A	.	.	.	.	.	C	.	.	.	.	.	.	G		A	.	.	.	G	G	.	.	.	.	.	.	.	1	KT964579
H13	.	.	.	.	.	.	C	.	.	.	G	.	A	.	.	.	G	.	C	.	.	.	.	.	.	.	.	A	.	.	.	.	G	.	.	.	.	C	.	.	4	KT964580
H14	.	.	.	.	.	.	.	.	.	.		.		.	.	A	.	.		.	T	.	.	T	.	.	.	.	.	.	.	.	G	.	.	.	.	.	.	.	1	KT964581
H15	.	.	.	.	.	.	.	.	.	.	G	.	A	.	.	.	.	T	C	.	.	.	.	.	.	.	G	A	.	.	T	G	G	.	.	.	.	.	.	.	4	KT964582
H16	.	.	.	.	C	.	.	.	.	.		.		.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	C	.	.	G	.	.	C	.	.	.	.	1	KT964583
H17	.	.	.	.	.	.	.	.	C	.	G	.	A	.	.	.	.	.	C	.	.	.	.	.	.	.	.	A	.	.	.	G	G	.	.	.	.	.	.	.	7	KT964584
H18	.	.	.	.	.	.	.	.	.	.	G	.	A	.	.	.	.	T	C	.	.	.	.	.	.	.	.	A	.	.	T	G	G	.	.	.	.	.	.	.	2	KT964585
H19	.	.	G	.	.	.	C	.	.	C	G	.	A	.	.	.	G	.	C	.	.	.	.	.	.	.	.	A	.	.	.	.	G	.	.	.	.	C	.	.	3	KT964586
H20	.	.	.	.	.	.	.	.	.	.	.	.	.	.	T	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	G	.	.	.	.	C	.	.	3	KT964587
H21	.	.	.	.	.	.	C	.	.	C	G	.	A	.	.	.	G	.	C	.	.	.	.	.	.	.	.	A	.	.	.	.	G	.	.	.	.	.	.	.	2	KT964588
H22	.	.	.	.	.	.	.	.	.	.	.	.	A	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	G	.	.	.	.	.	.	.	1	KT964589
H23	.	.	.	.	.	.	.	.	.	.	.	G		.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	G	.	.	.	.	.	.	.	1	KT964590
H24	.	.	.	.	.	.	.	.	.	.	.	.		.	.	.	.	.	.	.	.	.	C	.	.	.	.	.	.	.	.	.	G	.	.	.	.	.	.	.	2	KT964591
H25	.	.	.	.	.	.	.	.	.	.	G	.	A	.	.	.	.	.	C	.	.	.	.	.	.	.	.	A	.	.	.	G	G	.	.	.	.	.	C	T	1	KT964592
H26	G	.	.	.	.	.	.	.	C	.	G	.	A	.	.	.	.	.	C	.	.	.	.	.	.	.	.	A	.	.	.	G	G	.	.	.	.	.	.	.	1	KT964593
H27	.	T	.	.	.	.	.	.	.	.	G	.	A	.	.	.	.	.	C	.	.	.	.	.	.	.	.	A	.	.	.	.	G	.	.	.	.	.	.	.	1	KT964594
H28	.	.	.	.	.	.	.	.	.	.	.	.		A	.	.	.	.	.	C	.	.	.	.	G	.	.		.	.	.	.	G	.	.	.	.	.	.	.	1	KT964595
H29	.	.	.	.	.	.	.	.	C	.	G	.	A	.	.	.	.	.	C	.	.	.	.	.	.	.	.	A	.	.	.	.	G	.	.	.	G	.	.	.	1	KT964596
H30	.	.	.	.	.	.	.	A	.	.	.	.		.	.	.	.	.	.	.	.	.	.	.	.	.	.		.	.	.	G	G	.	.	.	.	.	.	.	1	KT964597
H31	.	.	.	.	.	.	.	.	.	.	G	.	A	.	.	.	.	T	C	.	.	.	.	.	.	.	G	A	A	.	T	.	G	.	.	.	.	.	.	.	1	KT964598
H32	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	T	.	.	.	.	.	.	.	G	G	.	T	.	.	.	.	.	1	KT964599
				PS		PS	PS		PS	PS	PS		PS				PS	PS	PS									PS	PS		PS	PS				PS		PS
Codon	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	1	2
AA1																																							S
AA2																																							L

Table 3. Genetic diversity of S. olivacea samples from four localities in the Bay of Bengal. “N_H” indicates number of haplotypes found from each location, “N” indicates number of individuals tested per locality, “h” indicates haplotype diversity, “p” indicates nucleotide diversity.

Location	N_H	N	h	p
SAT	10	23	0.866	0.005
KHU	6	20	0.805	0.005
BAG	17	21	0.976	0.007
COX	16	23	0.933	0.006
Average	16.2	30.2	0.900	0.006

Table 4. Genetic distance within (diagonal) and between (below diagonal) localities, and the fixation index (F_ST) between localities (above diagonal). “*” indicates P < 0.05.

Location	SAT	KHU	BAG	COX
SAT	0.0049	-0.0268	-0.0291	0.0574*
KHU	0.0049	0.0053	-0.0138	0.0824*
BAG	0.0058	0.0061	0.0070	0.0190
COX	0.0059	0.0060	0.0065	0.0058

Table 5. Analysis of molecular variance (AMOVA) of mtDNA COI sequence of S. olivacea in the Bay of Bengal

Source of variation	df	Sum of squares	Variance components	Percentage of variation	F_ST	P
Among populations	3	9.998	0.04226 Va	1.72	0.0172	0.15054
Within population	83	200.416	2.41465 Vb	98.28
Total	86	210.414	2.45691

Table 6. Neutrality tests for each population of S. olivacea, and for all populations pooled together in the Bay of Bengal. Values shown are: the observed and the expected value of the sum of haplotype frequencies (F_O and F_E), the observed and expected number of alleles (N_O and N_E), the Tajima D-statistic (D), and Fu’s F_S. Significant departures from the equilibrium neutrality.

Population	Ewens-Watterson		Chakraborty		Tajima's D	Fu's F_S
	F_O	F_E	N_O	N_E
SAT	0.172	0.168	10.000	8.190	-0.193	-1.069
KHU	0.235	0.295	6.000	8.011	0.196	2.239
BAG	0.070	0.070	17.000	9.379	-0.917	-8.163
COX	0.108	0.082	16.000	8.931	-0.935	-6.773
Total	0.089	0.063	32.000	14.841	-1.218	-14.119

Table 7. Mismatch distributions analysis of S. olivacea in the Bay of Bengal. “t” indicates units of mutational time, “q₀” indicates q before population growth, “q_t” indicates q after population growth, “SSD” indicates sum of the square deviations between the observed and expected mismatch, “P_SSD” indicates the probability of SSD, “Rag” indicates raggedness index, and “P_Rag” indicates the probability of raggedness.

Population	t	q₀	q_t	SSD(P_SSD)	Rag(P_Rag)
SAT	6.50195	10.37695	99999.00000	0.02535(0.28000)	0.05146(0.34000)
KHU	7.72070	11.24023	99999.00000	0.09395(0.07000)	0.21947(0.02000)
BAG	7.50391	10.88281	99999.00000	0.01039(0.55000)	0.02669(0.53000)
COX	7.05078	10.96094	403.60840	0.00316(0.95000)	0.00764(1.00000)
Average	7.19434	10.86523	75100.1521	0.03321(0.46250)	0.07632(0.47250)

Table 8 Summary of variation at ten microsatellite loci among N_S samples of S. olivacea from four locations in the Bay of Bengal. The number of observed alleles (N_A), inbreeding co-efficient (F_IS), average observed and expected heterozygosity (H_O and H_E), and probability of deviation from Hardy-Weinberg (P) as described. “*” indicates P < 0.05, “**” indicates P < 0.01, “***” indicates P < 0.001 and “ns” indicates not significant.

Population	Parameter	Microsatellite loci
Population	Parameter	Scpa01	Scpa03	Scpa04	Scpa05	Scpa09	Ss-101	Ss-103	Ss-112	Ss-403	Ss-513
SAT
	N_S	23	23	23	23	23	23	23	23	23	23
	N_A	13	3	3	18	5	24	12	7	12	18
	F_IS	-0.022	0.921	1.000	0.149	-0.043	-0.043	0.249	0.400	0.445	-0.104
	H_O	0.875	0.042	0.000	0.792	0.458	1.000	0.667	0.500	0.458	1.000
	H_E	0.856	0.519	0.667	0.927	0.440	0.959	0.883	0.826	0.817	0.908
	P	ns	***	***	**	ns	ns	**	***	***	**
KHU
	N_S	20	20	20	20	20	20	20	20	20	20
	N_A	4	3	2	10	2	16	9	5	13	6
	F_IS	-0.238	1.000	0.814	0.261	1.000	-0.043	0.204	0.583	0.276	0.196
	H_O	0.750	0.000	0.083	0.667	0.000	1.000	0.667	0.333	0.667	0.667
	H_E	0.612	0.522	0.431	0.891	0.159	0.960	0.830	0.779	0.909	0.822
	P	**	***	*	*	*	*	ns	***	**	ns
BAG
	N_S	21	21	21	21	21	21	21	21	21	21
	N_A	14	4	3	15	5	23	11	10	12	13
	F_IS	0.108	1.000	1.000	0.191	-0.002	0.043	0.169	0.487	0.353	-0.044
	H_O	0.792	0.000	0.000	0.750	0.417	0.917	0.750	0.458	0.542	0.958
	H_E	0.886	0.621	0.613	0.924	0.416	0.957	0.899	0.885	0.831	0.918
	P	*	***	***	***	ns	*	ns	***	**	**
COX
	N_S	23	23	23	23	23	23	23	23	23	23
	N_A	11	3	3	16	4	21	10	9	14	9
	F_IS	0.038	1.000	1.000	0.648	0.691	0.310	0.573	0.903	-0.084	0.657
	H_O	0.667	0.000	0.000	0.333	0.167	0.667	0.375	0.083	1.000	0.292
	H_E	0.692	0.401	0.649	0.933	0.532	0.959	0.867	0.844	0.924	0.839
	P	ns	***	***	***	***	**	***	***	ns	***

Total	Total N_S	87	87	87	87	87	87	87	87	87	87
	Total N_A	23	4	3	23	7	33	14	10	20	22
	Mean F_IS	0.013	0.977	0.981	0.322	0.296	0.083	0.312	0.596	0.233	0.164
	Mean H_O	0.771	0.010	0.021	0.635	0.260	0.896	0.615	0.344	0.667	0.729
	Mean H_E	0.834	0.710	0.668	0.942	0.442	0.965	0.884	0.868	0.913	0.922

Table 9. Analysis of molecular variance (AMOVA) for S. oilvacea from four populations based on microsatellite datasets.

Source of variation	d.f.	Sum of squares	Variance components	Percentage of variation	Fixation indices
Among populations	3	51.027	0.28559 Va	6.87	F_ST: 0.06871
Among individuals within populations	80	420.729	1.38849 Vb	33.41	F_IS: 0.35872
Within individuals	84	208.5	2.48214 Vc	59.72	F_IT: 0.40279
Total	167	680.256	4.15621

No competing interests reported.

Genetic population structure of mud crab (Scylla olivacea) in the Bay of Bengal inferred from mitochondrial DNA and microsatellite markers

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Result

Discussion

Conclusions

Declarations

References

Tables

Additional Declarations

Status:

Version 1