Fisheries Research 227 (2020) 105550 Contents lists available at ScienceDirect Fisheries Research journal homepage: www.elsevier.com/locate/fishres Investigation of genetic diversity and stock structure of Aristeus alcocki Ramadan, 1938 (Decapoda: Aristeidae) populations in the Indian coast with microsatellite markers T Paramasivam Purushothamana,b,*, Rekha Devi Chakrabortya, Maheswarudu Giddaa, Kuberan Ganesana a Crustacean Fisheries Division, Central Marine Fisheries Research Institute, Ernakulam North P.O., P.B. No. 1603, Cochin, 682018, Kerala, India Peninsular and Marine Fish Genetic Resources Centre, ICAR-National Bureau of Fish Genetic Resources, CMFRI Campus, Post Box No. 1603, Ernakulam North P.O., Kochi, 682 018, Kerala, India b A R T I C LE I N FO A B S T R A C T Handled by: J Viñas Aristeus alcocki (Arabian red shrimp) forms the commercially important deep-water trawl fishery in the Indian waters. This species has not been the focus of major genetic studies in spite of our previous report on its populations suggesting significant differences. Hence, we investigated the genetic diversity and stock structure of Arabian red shrimp using nine microsatellite markers with 203 individuals collected from five locations along the Indian coast form the period of 2013–2016. The observed and expected heterozygosity ranged from 0.70–0.98 (Mean: 0.85) and 0.56–0.85 (Mean: 0.69), respectively indicating significant genetic diversity among the sampled locations. Analysis of molecular variance (AMOVA) showed > 99 % of genetic variability within the individuals. Cluster analysis suggested the absence of genetic distances between the locations grouping all the individuals into a single cluster. Absence of large-scale genetic differentiation is probably governed by the mixing of waters along the coastline, habitat, currents and migratory patterns of these species have been discussed. Future studies on this species should consider it as a single stock for adopting fishery management strategies in the Indian coast. Keywords: Genomic DNA Indian Ocean Genetic variation Allele frequency Polymorphism Arabian red shrimp 1. Introduction Arabian Red shrimp Aristeus alcocki Ramadan, 1938 is taxonomically located in the order Decapoda, suborder Dendrobranchiata, and family Aristeidae which is broadly distributed in the Indian Ocean. It is found from the Gulf of Aden to Lakshadweep and Southwest coast of India to Andaman Sea through Bay of Bengal (Holthuis, 1980; Suseelan et al., 1989a, b; Pérez-Farfante and Kensley, 1997). This species preferably inhabits the muddy bottom of the upper continental slope between 230–3200 m depth (Alcock, 1901; Kurup et al., 2008). A. alcocki is one of the most important crustacean resources along the southern coast of India, which is usually fished with bottom trawls at 200–800 m depth. The remains practically unexploited in the northern coast of India (e.g., Rajan et al., 2001; Shanis Rajool et al., 2014; Chakraborty et al., 2015. According to the catch data series reported by CMFRI Central Marine Fisheries Research Institute, 2122 tons per annum of A. alcocki was landed during 2011–2016 and forms a profitable fishery with a profitability ratio of 0.35–0.38 (Shanis Rajool et al., 2014). However, the landing strategy showed a significant reduction in the catch volume during 2014–2016 (CMFRI, 2016). In fisheries management, understanding the genetic structure of a species forms the primary objective that will provide information to make management decisions and adopt viable protection measures to conserve and restore wild resources (Ferguson et al., 1995; Luo et al., 2015). Genetic variation is considered to be an important feature of the population to reveal not only the short term fitness of individuals but also the long term survival of the population, through allowing adaptation to the changing environmental conditions (Lande and Shannon, 1996). Information deduced from molecular markers can provide insight into genetic structure and geographical boundaries (i.e. breeding stock) and vulnerability (i.e. genetic diversity) of the species (BuchholzSørensen and Vella, 2016). Molecular markers have been proved to be an effective indicator of genetic variation within and between fishery populations of shrimp ⁎ Corresponding author at: Crustacean Fisheries Division, Central Marine Fisheries Research Institute, Ernakulam North P.O., P.B. No. 1603, Cochin, 682018, Kerala, India. E-mail address: purushothgene@gmail.com (P. Purushothaman). https://doi.org/10.1016/j.fishres.2020.105550 Received 8 July 2019; Received in revised form 22 February 2020; Accepted 27 February 2020 Available online 05 March 2020 0165-7836/ © 2020 Elsevier B.V. All rights reserved. Fisheries Research 227 (2020) 105550 P. Purushothaman, et al. molecular studies. species; Aristeus antennatus (Maggio et al., 2009; Cannas et al., 2012; Fernández et al., 2011b; Brutto Lo et al., 2012), Aristaeomorpha foliacea (Fernández et al., 2011a), Penaeus monodon (Mandal et al., 2012; Sekar et al., 2014) and Fenneropenaeus indicus (Sajeela et al., 2015). Microsatellite markers are characterized as co-dominant, highly polymorphic in nature and addition to their abundance, Even genomic distribution, small locus size, have quickly become useful molecular markers with great discriminatory power for the evaluation of genetic diversity in various species (Powell et al., 1996). The Indian peninsula (North Indian Ocean) can be divided into two smaller basins, the Bay of Bengal on the east and the Arabian Sea on the west. These marine biogeographic provinces differ in terms of mean sea surface tectonic model and sedimentary basins (Biswas, 2012). The Bay of Bengal region is having a positive water balance, where the huge amount of precipitation and river runoff highly exceeded than evaporation. The sediment in the shelf region is mostly clayey silt except towards the south, where it is sandy (Rao and Kessarkar, 2001). In the Arabian Sea, evaporation was exceeded precipitation and results indicate the negative water balance. The inner shelf region is characterized by clay, where in the outer shelf with sand (Rao and Wagle, 1997). Furthermore, the Arabian Sea is one of the greatest productive regions of the world. Although similar species composition between the provinces have long been recognized, studies have been discovered that several deep-water species that occur in peninsular India are characterised by homogeneity populations such as longtail tuna Thunnus tonggol (Swaraj et al., 2014) and Indian mackerel, Rastrelliger kanagurta (Sukumaran et al., 2017). Previous studies regarding the stock differences of A. alcocki was performed through truss morphometric analysis revealed that populations from various parts of the Indian coast showed less variation among populations of SEC (Chennai), SEN (Nagapattinam), SET (Tuticorin), SWS (Sakthikulangara) except for SWK (Kalamukku). This suggests, close relationship mainly due to the less variation in abdominal segments among these populations (Purushothaman et al., 2018a). The probable reasons hypothesized for this similarity were larval dispersal and long-distance migration for food, breeding and current patterns from the Arabian Sea to Bay of Bengal. Also, there have been few studies on molecular identification and phylogenetic reconstruction using mitochondrial DNA sequences that demonstrated no strong variation between different coast of India (Chakraborty et al., 2015; Chan et al., 2017). However, information concerning the genetic structure and variability of A. alcocki is not available. This work aimed to address the genetic diversity and stock structure of A. alcocki populations in the Indian coast. Analyses of microsatellite nuclear markers were used to describe the differences and distribution patterns of natural populations of this species. In addition, our results provide important information to design suitable management policies for the protection and enhancement of this fishery in a sustainable manner. 2.2. DNA extraction, amplification and genotyping of microsatellite loci The total genomic DNA was extracted from the pleopod of each individual using DNeasy® Blood & Tissue Kit (Qiagen Inc.) according to the manufacturer’s protocol. The cells were lysed by incubating at 56 °C for 2 h and all other steps were followed as per the protocol. The primers for nine nuclear microsatellite loci were taken from Cannas et al. (2008), where originally designed for the Aristeus antennatus. The microsatellite loci were optimised for genotyping by following of Palumbi (1996), and Cannas et al. (2008). The amplification of microsatellite markers were performed in 25 μl reaction cocktails containing genomic DNA (0.5 μg μl−1), Taq DNA polymerase (0.05 U μl−1), 1X buffer, MgCl2 (1.5 mM), 10 pM μl-1 of each primer and dNTPs (200 μM). The PCR thermal profile used was 94 °C for 5 min for initial denaturation, followed by 35 cycles of 94 °C for 1 min, annealing at 52–54 °C for 1 min, extension at 72 °C for 1.5 min, and a final extension at 72 °C for 5 min (Table 1). Amplification of PCR products were confirmed by electrophoresis on a 1.5 % agarose gel containing ethidium bromide and visualized under UV transilluminator (Lark, India). Analysis of fragment size was carried out by ABI prism genetic analyser (Applied Biosystems, USA) at AgriGenome Labs, Scigenom, Cochin, India. 2.3. Data analyses Allele frequency, the number of alleles (Na), observed (Ho), expected (He) heterozygosity and unbiased expected heterozygosity (UHe) per locus and locations were calculated with the computer program GenAlEx v. 6.41 (Peakall and Smouse, 2006). GENEPOP 4.0 package (Raymond and Rousset, 1995) was used to calculate deviations from Hardy-Weinberg equilibrium (HWE) for each locus and linkage disequilibrium between pairs of loci by using Fisher's exact test, under Markov Chain Monte Carlo (MCMC) algorithms (Guo et al., 1992), with 1000 dememorizations, 100 batches (treatments per location) and 10000 iterations per batch. Significance levels for both determinations were adjusted with the Bonferroni test for multiple comparisons with a significance level of p < 0.05 (Rice, 1989). FIS (Weir and Cockerham (1984) was calculated in GENEPOP 4.0 (Raymond and Rousset, 1995) with significance values for each locations. The presence of null alleles was tested with MICROCHECKER v 2.2.3 (Van Oosterhout et al., 2004). The FST values, relative to the null alleles and confidence intervals with and without correction were estimated with FREENA program (Chapuis and Estoup, 2007), if comparison of estimated FST values denoted significant difference, then any locus shows presence of null alleles in the sample should be discarded. Polymorphism information content (PIC) for each locus and locations were calculated using PIC –Calc 0.6 software (Nagy et al., 2012). ANOVA F statistic was used to detect the differences among the locations with the means values of Ho and UHe. To assess the genetic variation on among the populations and different geographic locations, pairwise Fst values were calculated and followed by statistical assessment of significance with 10,100 permutation steps for every comparison. Hierarchical analysis of molecular variance (AMOVA) was carried out using the program ARLEQUIN 3.5 (Excoffier and Lischer, 2010) to assess the presence of differential genetic structure. We performed a Bayesian cluster analysis to infer population structure and estimate the number of genetically distinct populations, using STRUCTURE v.2.2.3 (Pritchard et al., 2010) to determine the probabilistic assignments of samples based on genotypes to K sub populations. K estimation was completed using 20 independent simulations for K = 1–5 with 100,000 MCMC iterations and 10,000 batches. The most probable estimation of groups in the current dataset was done by using the ad hoc statistic DK method proposed by Evanno et al. (2005) and the value of K best fitting the data was selected using the log posterior probability of the data for a given K, Ln Pr (XjK) (Pritchard and Wen, 2004). 2. Materials and methods 2.1. Sampling A total of 203 individuals of A. alcocki were collected from five locations along the Southern coasts of India; i.e., Tuticorin (SET), Chennai (SEC), Nagapattinam (SEN) on the southeast, and Sakthikulangara (SWS), Kalamuku (SWK) on the southwest Indian coast (Fig. 1), the sampling period comprised from 2013 to 2016. Sampling sites were selected based on the major commercial fishing areas of the species. The specimens were obtained from the commercial catch that used deep-sea bottom trawlers with a cod-end mesh size of 20–26 mm, operated at a depth of 200–900 m. For morphological identification of A. alcocki followed the taxonomic keys of Alcock (1901), Crosnier (1978) and Suseelan (1989a). The collected specimens were preserved in 90–95 % of absolute ethanol until processing in the laboratory, for 2 Fisheries Research 227 (2020) 105550 P. Purushothaman, et al. Fig. 1. Sampling geographical locations used for the collection of A. alcocki specimens. South West Coast (Arabian Sea): Kallamuku ̶ SWK (1), Sakthikulangara ̶ SWS (2); South East Coast Bay of Bengal) (: Tuticorin ̶ SET (3), Nagapattinam ̶ SEN (4), Chennai ̶ SEC (5). Table 1 Details of the amplified microsatellite loci used in this study for population analysis of Aristeus alcocki. All the respective primers were reported by Cannas et al. (2008). Locus Primer sequence (5’-3’) Modif Ta (0C) Allelic sizes (pb) Fluorescent Marker Ant 20 F: TAGTGTTCCATAGACTTATA R: ACTAGACAAATCTAAATGCT F: TGCTGATACAGAAGGTAGGC R: TTGGTACTGTTTCCCCATGC F: ATGGATTCAATAATTTCGGC R: ATATTCTGGCATTTTGTAGG F: GAGGTGTAGGCAGAGTGA R: GCCTCTTTTACGTTACGCTG F: TGAGACCCTCAGACTCAC R: TCTTTCTTTCTACTTCCCCTC F: TGTACGGGGCGACAGTCTAC R: GGGGAGACGGCGAAGCAAAC F: TGTTACGATTCCTGGTAAGG R: ATGAAGTGGTTGAGTAGTCC F: AACGTGCCAATCAAAGTGAT R: TGAGGTAGAGACAAAGACTG F: TGTCATAGCGGCTTCCAT R: ATATCTTGTTACGACCCTCG (CA)7 54 213-227 HEX (AC)10 52 208-224 FAM (CA)8 52 148-188 TET (TG)12 54 151-223 TET (GT)9 54 128-168 FAM (TG)11 53 260-282 FAM (AC)10 53 196-216 HEX (AC)9 53 230-250 TET (TG)4CT(TG)5 CG(TG)3 52 232-254 HEX Ant 93 Ant 194 Ant 94 Ant 16 Ant 37 Ant 82 Ant 34 Ant 99 3. Results ranged from 0.57–0.88 (Mean: 0.70). From ANOVA and Mann-Whitney U-tests: Ho, He and means of allelic number showed no significance differences among sampling locations. The HWE tests showed that 40 (88.8 %) of the 45 locus-location combinations were generally consistent with Hardy-Weinberg proportions. Loci ant82 exhibited the significant deviation on all locations except SEC. The programme FREENA showed that the presence of few null alleles in different loci. A Student-t test (p > 0.05) showed no difference between the FST values considering/not considering null alleles (FST = 0.010757, and FST = 0.010795, respectively). Then, the original data were taken into account directly for further analysis. The pattern of genetic differentiation among populations observed from pair-wise FST and Nei tests were represented in Table 3. According to pair-wise distance, the FST ranged from 0.001 (between SWK and In present study, a total of 203 individuals were successfully genotyped using nine microsatellite loci exhibited good amplification. These loci were polymorphic for populations of the five locations. Therefore, the observed PIC ranged from 0.863–0.874 (Mean: 0.869) (Table 2). There was no linkage disequilibrium between the pair of loci. The observed allele sizes ranged from 128 to 282 bp and the mean alleles per loci ranged from 5 to 9.8 (Mean: 6.3). The number of alleles per locus ranged from 4 in the Ant16, Ant20 and Ant94 markers to 12 for marker Ant99. The number of alleles per location varied slightly, the lowest value observed in SWS with 51 and highest in the SWK with 64. The observed and expected heterozygosity for nine loci were 0.70–0.98 (Mean: 0.85) and 0.56–0.85 (Mean: 0.69) respectively, the UHe was 3 Fisheries Research 227 (2020) 105550 P. Purushothaman, et al. Table 2 Genetic diversity of A. alcocki sampled from five locations along the southern coast of India as analyzed by nine microsatellite loci. South west coast: Kallamuku ̶ SWK, Sakthikulangara ̶ SWS; South east coast: Tuticorin ̶ SET, Nagapattinam ̶ SEN, Chennai ̶ SEC; Na ̶ Allele number per locus; HWE ̶ HardyWeinberg equilibrium; Ho ̶ Observed heterozygosity; He ̶ Expected heterozygosity; UHe ̶ Unbiased expected heterozygosity; FIS ̶ Coefficient of endogamy per locus; PIC ̶ Polymorphism information content. Locus Ant99 Ant16 Ant20 Ant34 Ant37 Ant82 Ant93 Ant94 Ant194 Na HWE Ho He UHe FIS Na HWE Ho He UHe FIS Na HWE Ho He UHe FIS Na HWE Ho He UHe FIS Na HWE Ho He UHe FIS Na HWE Ho He UHe FIS Na HWE Ho He UHe FIS Na HWE Ho He UHe FIS Na HWE Ho He UHe FIS PIC SWK (n = 41) SWS (n = 36) SET (n = 41) SEN (n = 42) SEC (n = 43) 12 0.442 0.902 0.855 0.865 −0.043 6 0.204 0.854 0.693 0.702 −0.220 5 0.054 0.927 0.740 0.749 −0.24 6 0.054 0.854 0.682 0.690 −0.24 11 0.938 0.829 0.795 0.805 −0.03 7 0.000 0.976 0.714 0.723 −0.35 6 0.071 0.854 0.627 0.635 −0.35 6 0.418 0.780 0.579 0.586 −0.33 5 0.056 0.780 0.681 0.690 −0.13 0.863 9 0.125 0.720 0.831 0.848 0.153 4 0.135 0.840 0.580 0.592 −0.43 5 0.176 0.920 0.623 0.636 −0.46 5 0.106 0.800 0.668 0.682 −0.17 8 0.435 0.880 0.822 0.838 −0.05 6 0.02 0.960 0.718 0.732 −0.31 5 0.066 0.880 0.690 0.704 −0.25 4 0.060 0.840 0.560 0.571 −0.48 5 0.594 0.760 0.675 0.689 −0.10 0.874 9 0.720 0.767 0.784 0.797 0.038 5 0.145 0.900 0.706 0.718 −0.25 4 0.066 0.833 0.695 0.707 −0.18 6 0.604 0.767 0.632 0.643 −0.19 6 0.071 0.933 0.737 0.749 −0.25 8 0.000 0.987 0.779 0.792 −0.26 7 0.720 0.800 0.699 0.711 −0.12 5 0.146 0.900 0.613 0.623 −0.45 6 0.056 0.700 0.666 0.677 −0.03 0.870 11 0.071 0.868 0.871 0.883 0.016 6 0.165 0.895 0.714 0.724 −0.24 6 0.128 0.947 0.632 0.641 −0.48 6 0.141 0.789 0.686 0.695 −0.13 6 0.856 0.842 0.788 0.798 −0.05 5 0.003 0.974 0.691 0.700 −0.39 5 0.085 0.842 0.653 0.662 −0.27 5 0.134 0.789 0.583 0.591 −0.34 6 0.606 0.763 0.710 0.720 −0.06 0.868 8 0.111 0.950 0.819 0.830 −0.140 4 0.005 0.900 0.698 0.707 −0.27 5 0.056 0.875 0.709 0.718 −0.22 7 0.990 0.750 0.637 0.645 −0.16 7 0.244 0.825 0.813 0.824 −0.001 7 0.244 0.900 0.658 0.666 −0.35 6 0.508 0.800 0.633 0.672 −0.19 5 0.087 0.800 0.570 0.577 −0.39 7 0.563 0.775 0.667 0.675 −0.14 0.872 Table 3 Value matrix comparing pairs of Locations. Above the diagonal, values of genetic distances of Nei; under the diagonal, genetic differentiation paired with FST values. Locations SWK SWS SET SEN SEC SWK SWS SET SEN SEC 0 0.0132 0.0133 0.0133 0.0011 0.071 0 0.0023 0.0039 0.0036 0.070 0.049 0 0.0074 0.0075 0.066 0.048 0.055 0 0.0090 0.032 0.046 0.053 0.053 0 genetic variation was observed at the regional level (0.3 %, Fst = 0.00353) (Table 4). In addition, the Fst value analyses of microsatellite data by the STRUCTURE clustering algorithm suggested the absence of genetic distance (Fig. 2) between the locations grouping all the individuals into a single cluster. Testing K range from one to five, K = 2 was detected with maximum Delta-K value for the whole dataset. 4. Discussion The microsatellite markers are widely used to study genetic variation in populations based on its characteristics of biparental inheritance (except for sex chromosomes), codominant, multiallelism, genomic distribution, high polymorphism and their high level of allelic diversity due to high mutation rates (Kijas et al., 1994; Chase et al., 1996; Engstrom et al., 2007). The selected markers were initially designed for A. antennatus; reported to be polymorphic and highly informative. Similarly, selected markers for the present study showed greater polymorphism (mean: 0.86), which was higher than the standard value (0.5: Botstein et al., 1980) indicating that these markers are effectively suitable in characterising the population structure of A. alcocki. The high level of genetic diversity for a given species indicates the ability of the species to adapt easily to the environment (Davies et al., 2016). Dewoody and Avise, 2000 said that the expected and observed heterozygosity and the large number of alleles are general indicators of genetic diversity. In A. alcocki the mean number of alleles per loci, mean of observed and expected heterozygosity ranged from 5 to 9.8, 0.85 and 0.7 across the 9 microsatellite locus which was almost similar to A. antennatus (7.3–15, Ho = 0.38 – 0.79) reported from Western Mediterranean Sea (Cannas et al., 2012). However, these values are smaller in range compared to the findings reported in P. monodon (20.3–41.2, Ho) sampled from Indo-Pacific region (You et al., 2008; Mendal et al., 2012); and in Fenneropenaeus indicus (2–26, H0 = 0.14–0.8, He = 0.2–0.9) from southwest and southeast coast of India (Sajeela et al., 2015). Further, even higher microsatellite allele variation has been recorded in a number of marine fishes which included Sardinella longiceps (15.6–58.8 alleles/loci) using six microsatellite loci screened from the Indian coast and Gulf of Oman (Sebastian et al., 2017). The mean value for observed heterozygosity (0.85) was noticed to be higher than UHe (0.7) in A. alcocki (Table 2) while A. antennatus showed lesser values for Ho than the UHe (Cannas et al., 2012). Although the species analysed and the markers are different in the present study compared to other works (Sajeela et al., 2015; Sebastian et al., 2017) it is worth to consider that all these studies reported the existence of homogeneous populations. However, genetic diversity may be driven by many bottlenecks viz., natural selection force against heterozygotes (Selkoe et al., 2006), over–exploitation pressure (Bergh and Getz, 1989), water pollution (Dudgeon et al., 2006), destruction or degradation of habitat (Dudgeon et al., 2006), or their combined influences. In the past decade overfishing was noticed to be the critical problem in China (Fu et al., 2003; Kang et al., 2009) and reported to be the main reason for the decrease in population size, yield, genetic diversity and ultimately leading to the loss in population viability (Hauser et al., 2002). The pairwise FST, Nei, and AMOVA values calculated from SEC) to 0.0133 (between SWK and SET, SEN), while Nei distance values ranged from 0.032 (between SWK and SEC) to 0.071 (between SWK and SWS) (Table 3). Pairwise comparisons showed that the Nei distance values among populations were consistently larger than those of FST, both values reflected identical patterns of genetic distances among the populations. Results of AMOVA analysis of the whole dataset explained 99.22 % of the total variation within individuals and 0.78 % among locations, which was not significant (Fst = 0.00780) and further, no 4 Fisheries Research 227 (2020) 105550 P. Purushothaman, et al. Table 4 Analysis of Molecular variation (AMOVA) for 5 locations of Aristeus alcocki. Hypothesis Source of variation df Sum of squares Variance components Percentage of variation Fixation indices Unstructured Among populations Within populations Among populations Within populations 4 401 1 404 19.693 1094.399 5.064 1109.028 0.02510 3.19067 0.01134 3.20528 0.78 99.22 0.35 99.65 FST : 0.00780* Two Regions (SWK, SWS)(SET, SEN, SEC) FST : 0.00353* * P-value = < 0.05. Fig. 2. Cluster analysis using the program STRUCTURE (K = 2) for A. alcocki with microsatellite data in the Indian coast. Its associated probability of belonging to one of the two genetic clusters detected (Red and Green). (A) unstructured, (B) Structured regional wise; South west coast (SW), South east coast (SE), Kallamuku (SWK), Sakthikulangara (SWS); Tuticorin (SET), Nagapattinam (SEN), Chennai (SEC) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). Southwest monsoon (June to September), West India Coastal Current (WICC) flows southward and feed to EICC with the help of Summer Monsoon Current (SMC) carrying high saline AS waters (Shetye, 1999; Vinayachandran et al., 2005) exhibiting many biological implications (Jyothibabu et al., 2008). The resulting salinity and nutrient exchange processes between the atmosphere, surface and deep waters were eventually affect the biological and biochemical processes (Ittekot et al., 1991; Prasannakumar et al., 2002). Such a mixing of the marine regions might play an important role on the population structure of A. alcocki in BoB and AS basin of the Indian coast, also which has to be revealed from future studies. Similarly the reports on Indian oil sardine (Sukumaran et al., 2016) and Indian mackerel (Sukumaran et al., 2017) presumed homogeneity in fish populations in Indian waters due to mixing of marine regions and long distance migrations. During the southwest monsoon, the biological signatures of upwelling process along the southwest coast of India is characterized by vertical mixing phenomena and cascading flows of denser upper layers enriching the deeper waters with organic nutrients (phytoplankton and zooplankton swarms) (Madhupratap et al., 1990). Also part of the upwelled waters moves into the Gulf of Mannar and flow towards the Bay of Bengal (Shetye and Gouveia, 1998; Vinayachandran, 2004). This pattern of vertical mixing might be contributing an important role in shaping the population structure of A. alcocki in the southwest and southeast coast of India. In conclusion, the genetic structure of Arabian red shrimp shows that no significant difference in populations along the Indian coast (Arabian and Bay of Bengal waters) which is probably influenced by the mixing of waters along the coastline, habitat, currents and migratory patterns of A. alcocki. However, our previous studies using truss measurements did find that presence of significant difference in SWK population due to the variability in the abdominal characters (Purushothaman et al., 2018a), this may be presumed to the availability of food content in the environment, which has to be studied in detail in future. The present study indicated the presence of high genetic diversity which forms a prerequisite for adaptive potential and selective responses. An assessment of the genetic diversity and stock structure of microsatellites indicated the absence of significant variation among the samples of A. alcocki collected from the South west (Arabian Sea) and South east (Bay of Bengal) coast of India. Moreover the results of AMOVA also indicated the proportion of genetic variation was mainly associated to differences among the individuals (99.2 %) with Fst = 0.0078 which is further confirmed by the cluster analysis performed using STRUCTURE (Fig. 2.) directed towards the presence of homogeneous groups due to the absence of specific allelic variation in the sampled localities. The present study was in agreement with the results reported in A. antennatus (among individual difference 99.3 %; Fst = 0.0067) using same markers in the Mediterranean Sea (Cannas et al., 2012) where no genetic differentiation was noticed between the localities. In the present study, lack of significant differentiation among sample locations are commonly attributed to a gene flow sufficient to prevent cases of genetic drift or selective actions. Penaeoid shrimps did not carry the fertilized eggs in pleopods, instead eggs and larvae are released directly into the pelagic environment. It can be transported by ocean currents which can promote long distance dispersal and gene flow across a wide geographic scale (Dall et al., 1990; Carbonell et al., 2010). Moreover, A. alcocki showed year around spawning activity releasing 131,750 ova/female (Purushothaman et al., 2018b), favouring the gene flow. In addition to the above observations, A. alcocki is known to exhibit particular mobility pattern by way of vertical and horizontal displacements in search of food and adjusting to the ecological fluctuations (parameters of water, salinity and temperature) may the reason for homogeneous populations. Migration also could play an important role in dispersion, especially if associated to the deep-water circulation pattern in the Peninsular India. The currents along the Bay of Bengal (BoB) and Arabian Sea (AS) waters were found linking the open ocean monsoon currents with the circulation patterns. During the Northeast monsoon (November to February), East India Coastal Current (EICC) and Winter Monsoon Current (WMC) maintained the continuous flow from the northern BoB to the northern AS (Fig. 3.) having low salinity (Shetye and Gouveia, 1998; Shetye, 1999; Shenoi, 2010; Rao et al., 2008). During the 5 Fisheries Research 227 (2020) 105550 P. Purushothaman, et al. Fig. 3. Ocean currents around the Indian subcontinent; Southwest Monsoon period (A), Northeast Monsoon period (B), WICC- West India coastal Current, LLLakshadweep Low, LH- Lakshadweep High, SMC – Summer Monsoon Current, WMC- Winter Monsoon Current, EICC – East India Coastal Currents. a species can contribute immensely for the proper management and conservation of this commercially important fishery resource. Future studies on this species should consider it as a single stock for adopting fishery management strategies in the Indian coast. (Risso, 1816) (Decapoda, Dendrobranchiata), as inferred from amplified length polymorphism (AFLP) and mtDNA analyses. Crustaceana 85, 1393e1408. Buchholz-Sørensen, M., Vella, A., 2016. Population Structure, Genetic Diversity, Effective Population Size, Demographic History and Regional Connectivity Patterns of the Endangered Dusky Grouper,Epinephelus marginatus (Teleostei: Serranidae), within Malta’s Fisheries Management Zone. 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