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Genetic diversity and population structure of the red stingray, Dasyatis akajei inferred by AFLP marker
Biochemical Systematics and Ecology 51 (2013) 130–137
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Biochemical Systematics and Ecology journal homepage: www.elsevier.com/locate/biochemsyseco
Genetic diversity and population structure of the red stingray, Dasyatis akajei inferred by AFLP marker Ning Li a, Na Song a, Guang-ping Cheng b, **, Tian-xiang Gao a, * a b
Institute of Evolution and Marine Biodiversity, Ocean University of China, Qingdao 266003, China College of Animal Science and Technology, Guangxi University, Nanning 530004, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 19 March 2013 Accepted 9 August 2013 Available online
To examine population genetic diversity and variation of the red stingray Dasyatis akajei, samples from 1 freshwater region and 6 coastal localities within its distribution range were analyzed by using amplified fragment length polymorphism (AFLP) technology. A total of 207 loci were identified by 4 primer combinations from 87 individuals, 174 of which were polymorphic (84.1%). A high level of Nei’s gene diversity was observed with the overall value of 0.230 0.179. No significant genealogical clusters associated with sampling sites were revealed on the UPGMA tree. Both analysis of molecular variance (AMOVA, FST ¼ 0.085, P ¼ 0.00) and pairwise FST indicated significant genetic differentiation among four marine samples. However, no particular genetic differentiation was detected between marine and the limited sampling freshwater populations (AMOVA, FCT ¼ 0.056, P > 0.05). Except for the TZ vs. WZ (5.193), the gene flow estimates (Nm) demonstrated the effective immigrants were 1.918-2.976, suggesting low level dispersal between pairwise marine populations. Species-specific habits (demersal and sluggish habits) are probably responsible for the population structuring. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Dasyatis akajei AFLP marker Genetic diversity Genetic structure
1. Introduction The red stingray, Dasyatis akajei, is a typical demersal elasmobranch in the family Dasyatidae and is distributed coastally along the northwestern Pacific Ocean, from Japan to China (Nishida and Nakaya, 1990; Huveneers and Ishihara, 2006), even presumably occurred as far as Thailand, the Philippines, Fiji and Tuvalu (FishBase, 2009). D. akajei can inhabit not only in coastal area but in some rivers of Guangxi Province of China (Lin and Zeng, 1987), where the freshwater populations are deemed to be ‘land-locked’. Characterized by high longevity and low fecundity, D. akajei is considered highly vulnerable to habitat destruction and over-exploration. The red stingray has been listed as ‘Near Threatened’ species on the IUCN Red List (Huveneers and Ishihara, 2006) and its freshwater population is also defined as National Class II Protected Animals of China. As predominantly live-bears, the dispersal of rays is not dependent on the release of pelagic eggs and larvae, but determined by the vagility of adults. For coastal species of elasmobranch, the dispersal may be limited by their complex interactions with coastal environments (such as use of nursery areas) (Simpfendorfer and Milward, 1993). Accumulating evidence has suggested that the species-specific behavior, such as habitat preferences and/or site fidelity, is operated as the potential isolation factor in shaping the pattern of genetic structure of elasmobranch along continuous coastline (Keeney
* Corresponding author. Tel.: þ86 532 8203 2063; fax: þ86 532 8203 2076. ** Corresponding author. Tel.: þ86 771 323 5256. E-mail addresses: [email protected] (G.-p. Cheng), [email protected] (T.-x. Gao). 0305-1978/$ – see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.bse.2013.08.009
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et al., 2005; Le Port and Lavery, 2012). Population genetic differentiation over smallest scale has been demonstrated in the Pacific angel shark Squatina californica, which might be attributed to a combination of behavioral characteristics and topography of the sea floor (Gaida, 1997). With benthic and sluggish habits, the gene flow by dispersal among D. akajei populations might be not expected. Thus, further research is needed to explain the genetic variations among populations. Detecting genetic variation and population structuring of species has attracted much attention, since the genetic composition is usable for the assessment of genetic diversity of natural populations (Keiper and McConchie, 2000). It is suggested that population heterozygosity levels are linked directly to evolutionary potential and population fitness, and need to be kept to minimize their risk of decline or extinction (Reed and Frankham, 2003). Further, the accurate delimitation of species genetic diversity and population structure is in an important resource recovery need and can provide the essential baseline information that is desirable for appropriate fisheries management and conservation (Beheregaray, 2008). For instance, genetic investigations aided the resurrection of the maskray genus Neotrygon for a group of Indo-Pacific dasyatids which displayed considerable mitochondrial DNA (mtDNA) genetic diversity (Last et al., 2010). Previous studies of D. akajei only focused on the histology and biochemistry, and no attention to genetic investigations have been paid till now. On account of the D. akajei resource in a crisis, understanding its genetic diversity and population structure is a high priority for conservation of biodiversity and sustainable long-term management. Nuclear markers, focusing on hyper-variable regions of the genome, offer increased resolving power for the genetic polymorphism and population structure missed by mtDNA haplotypes on a finer scale (Bazin et al., 2006; Dudgeon et al., 2012). Among the several nuclear marker systems, AFLP (Vos et al., 1995) is highly efficient for the assessment of genetic variation pattern among and within populations (Travis et al., 1996). As a multi-locus fingerprinting technique, AFLP does not need previously known genetic information, a feature especially useful for those species for which both polymorphic markers in relation to population identity and sequence information are limited (Ajmone-Marsan et al., 2001). Up to now, AFLP marker has been applied to detect the genetic diversity and genetic structuring pattern in a growing body of marine organisms (Lerceteau and Szmidt, 1999; Mickett et al., 2003; Liu et al., 2009). In the present study, we presented a population genetic survey for both freshwater and marine populations of D. akajei using AFLP technique. Our aim was to investigate genetic diversity and characterize population structuring of D. akajei, and further to provide potential implications for the effective fishery management and conservative strategies. 2. Materials and methods 2.1. Sample collection Eighty-seven samples from 7 localities were collected within the distribution area of D. akajei from 2004 to 2008. Among them, four individuals were sampled from the Longzhou in River Guangxi of China. The remainders were collected along the coast of China but included a population recovered from Maizuru in Japan (Fig. 1). All individuals were defined morphologically. A fraction of caudal fin were excised and preserved in 95% ethanol for DNA extraction. Moreover, one specimen from its congener Dasyatis bennetti and two from Dasyatis izuensis, were also collected to serve as outgroups for phylogenetic analysis. 2.2. Genomic DNA extraction and AFLP method Total genomic DNA was isolated from muscle tissue by proteinase K digestion followed by a standard phenol-chloroform method. Procedures of AFLP were essentially based on Vos et al. (1995) and Wang et al. (2000). About 100 ng genomic DNA was digested with 1 unit of EcoR I and Mse I (NEB) at 37 C for 6 h. Double-stranded adapters were ligated to the restriction fragments at 20 C overnight after adding 1 mL 10 ligation buffer, 5 pmol EcoR I adapter (EcoR I-1/EcoR I-2; Table 1), 50 pmol Mse I adapter (Mse I-1/Mse I-2; Table 1), 0.3 unit of T4 DNA ligase (Promega) with a final volume of 10 mL. Preamplification PCR reaction was conducted using an Eppendorf Thermocycler (Mastercycler 5334) with a pair of primers containing a single selective nucleotide. Amplification was performed at an annealing temperature of 53 C for 30 s. The 20 mL PCR product mixture was diluted 10-fold with distilled water and used as templates for the subsequent selective PCR amplification. The selective amplifications were carried out in 20 mL PCR reaction volume containing 1 mL productions of preamplifications, 1 PCR reaction buffer, 150 mM of each dNTP, 30 ng of each selective primer, and 0.5 unit of Taq DNA polymerase on a gradient thermal cycler (Mastercycler 5334) with a touchdown cycling profile of nine cycles of 30 s at 94 C, 30 s at 65 C (1 C at each cycle), and 30 s at 72 C followed by the cycling profile of 28 cycles of 30 s at 94 C, 30 s at 56 C, and 1 min at 72 C. The final step was a prolonged extension of 7 min at 72 C. PCR products were run on 6.0% denaturing polyacrylamide gel electrophoresis (PAGE) for 2.5 h at 50 C on the Sequi-Gen GT Sequencing Cell (Bio-Rad, USA), and finally detected using the silver staining technique modified from Merril et al. (1979). Sequences of AFLP adapters and primers are listed in Table 1. Four primer combinations (E-ACG/M-CAA, E-ACG/M-CAG, E-ACG/M-CTA, E-ACG/M-CTC) were chosen for AFLP analysis (Table 1). 2.3. Data analysis Excluding smeared and weak bands by visual inspection, clear and unambiguous AFLP bands were recorded for binary data (1 ¼ presence, 0 ¼ absence), and a matrix was generated based on the binary characters. To estimate the genetic diversity of D. akajei, POPGENE software (Yeh et al., 1999) was used to calculate the number of polymorphic loci, proportion of
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Fig. 1. Sampling sites of D. akajei. C ¼ sampling sites for marine populations and : ¼ sampling sites for the freshwater population.
polymorphic loci and Nei’s genetic diversity. Similarity indices were calculated using the formula S ¼ 2Nab/(Na þ Nb) (Nei and Li, 1979), where Na and Nb are the number of bands in individuals a and b, respectively and Nab is the number of sharing bands. Genetic distances between individuals were computed using the formula D ¼ ln S (Nei and Li, 1979). Genetic relationships among individuals were estimated by constructing UPGMA tree based on Nei’s genetic distance (Nei, 1978) in Mega 4.0 (Kumar et al., 2008). To evaluate the genetic differentiation among populations (limited sampling populations were not analyzed), pairwise fixation index FST was calculated as implemented in ARLEQUIN (Schneider et al., 2000). Significance of pairwise FST comparisons was tested by 10,000 permutations and subsequently adjusted by sequential Bonferroni correction (Rice, 1989). Analysis of molecular variance (AMOVA) (Excoffier et al., 1992) was employed to further test the significant population structure as well as the geographical pattern of population subdivision (Excoffier et al., 1992). The gene flows were estimated by the equation: Nm¼(1 FST)/4FST (Wright, 1951), where Nm is the number of effective immigrants per generation.
Table 1 Adaptors and primer combinations sequences used in the study. Primer Adapters
Sequence EcoR I-adapter Mse I-adapter
Pre-amplification primer Selective amplification primer
EcoR I Mse I E-ACG/M-CAA E-ACG/M-CAG E-ACG/M-CTA E-ACG/M-CTC
50 -CTCGTAGACTGCGTACC-30 50 -AATTGGTACGCAGTCTAC-30 50 -GACGTGAGTCCTGAG-30 50 -TACTCAGGACTCAT-30 50 -GACTGCGTACCAATTC-30 50 -GATGAGTCCTGAGTAA-30 50 -GACTGCGTACCAATTCACG-30 50 -GATGAGTCCTGAGTAACAA-30 50 -GACTGCGTACCAATTCACG-30 50 -GATGAGTCCTGAGTAACAG-30 50 -GACTGCGTACCAATTCACG-30 50 -GATGAGTCCTGAGTAACTA-30 50 -GACTGCGTACCAATTCACG-30 50 -GATGAGTCCTGAGTAACTC-30
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Table 2 Number of bands generated by four primer combinations.
Number of loci Number of polymorphic loci Proportion of polymorphic loci
M-CAA/E-ACG
M-CAG/E-ACG
M-CTA/E-ACG
M-CTC/E-ACG
Total
40 32 80.0%
41 32 78.1%
64 59 92.2%
62 47 75.8%
207 174 84.1%
To examine the isolation by distance (Slatkin, 1993), pairwise values of FST/(1 FST) were plotted against geographical distance (one-dimensional stepping-stone model) between four marine populations (TZ, WZ, ND and ZZ). The strength and significance of the relationship between genetic distances and geographic distances was assessed using reduced major axis (RMA) regression and Mantel tests using IBDWS (Isolation by distance web service at http://phage.sdsu.edu/wjensen/) (Bohonak, 2002; Jensen et al., 2005). 3. Results In eighty-seven individuals from seven populations, a total number of 207 bands were identified by four AFLP primer combinations, of which 174 bands were polymorphic (84.1%) (Table 2). For each primer pair combined, the number of polymorphic loci scored ranged from 32 to 59 with the spectrum of proportion of polymorphic loci from 75.8% to 92.2% (Table 2). Across all individuals, the highest average genetic similarity but the lowest average genetic distance (0.131) were indicated by TZ population (0.877), while the contrary by Ma population (0.188/0.829). Of all the seven populations, the genetic diversity estimation for FZ, CZ and Ma populations, small in size, may be of no comparability with others and would not be further considered. In term of the four marine populations (TZ, WZ, ND and ZZ), ND population showed the greatest percent of polymorphic loci (68.9%), and the lowest was represented by TZ population (64.2%). Assuming Hardy–Weinberg equilibrium, ZZ and TZ populations possessed the highest (0.254 0.209) and the lowest gene diversity (0.198 0.195) respectively (Table 3). UPGMA tree rooted with D. bennetti and D. izuensis was constructed based on Nei’s genetic distance (Fig. 2). Within the D. akajei populations, there appeared to be no significant genealogical clustering with correspondence to sampling localities overall, except for several geographical subclades. Based on one-gene pool that grouped the four marine populations (TZ, WZ, ND and ZZ) for AMOVA, the result indicated genetic variation primarily existed within populations (91.5%) and 8.5% among populations. A weak but significant genetic structure among populations was detected by AMOVA (FST ¼ 0.085, P ¼ 0.00) as revealed by pairwise FST values. The greatest magnitude of genetic differentiation occurred among the geographically distant populations TZ vs. ZZ (FST ¼ 0.115, P < 0.01), meanwhile the lowest between adjacent populations TZ vs. WZ (FST ¼ 0.046, P < 0.01) (Table 4). Comparably, pairwise gene flow estimates (Nm) among populations varied from 1.918 (TZ vs. ZZ) to 5.193 (TZ vs. WZ). No genetic differentiation (P > 0.05) was detected between marine and freshwater populations by two gene pools-based analysis (AMOVA, FCT ¼ 0.056, P > 0.05) (Table 5). Moreover, Mantel test showed no significant correlation of genetic divergence with geographical distance (r ¼ 0.693, P > 0.05) (Fig. 3). 4. Discussion Estimates of genetic diversity provide a measure of an organism’s adaptive potential and survivability in respond to environmental change (Ryman et al., 1995; Schmitt and Hewitt, 2004). Based on the AFLP marker, investigation on genetic diversity of D. akajei populations presented a high level of Nei’s index and the proportion of polymorphic loci in comparison with the current reported studies on elasmobranches (Pasolini et al., 2011). The result was compatible with the mtDNA-based estimates of a high level of haplotype diversity, which appeared to be a rare phenomenon for a species undergoing resource declining. However, recessive populations seem to retain historic levels of genetic diversity if the decline has occurred recently (Roman and Palumbi, 2003; Bowen et al., 2007). As an incidental catch of commercial fisheries, no direct resource investigation was carried out for D. akajei, of which the unoptimisticable status in genetic diversity implied the conservation of this species can not be eased. Table 3 Sampling information of D. akajei including sampling sites, date of collection, sample size and several genetic diversity indices. ID
Population
Sampling date
Sample size
Number of loci
Number of polymorphic loci
Proportion of polymorphic loci
Average genetic similarity/average genetic distance
Nei’s gene diversity
TZ WZ ND FZ ZZ CZ Ma
Taizhou, Jiaojiang, China Wenzhou, Dongtou, China Ningde, Xiapu, China Fuzhou, Pingtan, China Zhangzhou, Dongshan, China Chongzuo, Longzhou, China Maizuru, Japan
October 2008 April 2009 October 2008 October 2008 October 2008 August 2004 April 2005
19 17 22 3 16 5 5
182 193 187 199 157 147 161
120 137 133 50 125 67 85
64.2% 68.8% 68.9% 31.9% 68.7% 45.6% 52.8%
0.877/0.131 0.851/0.161 0.870/0.139 0.873/0.136 0.830/0.187 0.865/0.145 0.829/0.188
0.198 0.225 0.220 0.122 0.254 0.179 0.192
0.195 0.198 0.193 0.187 0.209 0.214 0.205
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Fig. 2. UPGMA cluster analysis based on Nei’s genetic distances for seven populations of D. akajei. D. bennetti and D. izuensis were selected as outgroups to root the tree, and bootstrap support values were shown besides the internal branches.
Most genetic analyses of continuously distributed coastal species exhibit a trend toward high dispersal and low level of genetic structure (Graves, 1998; Duncan et al., 2006). Extraordinarily, D. akajei populations displayed a weak but significant genetic structure across short costal distances, as evidenced by pairwise FST and Nm between four marine populations and further by AMOVA analysis, supporting limited gene flow among populations of D. akajei. The significant population structuring was also evident as indicated by mtDNA-based indices (pairwise FST and AMOVA FST, Li et al., unpublished data), which were higher than the corresponding values calculated using AFLP data. Likely, the indices between these two marker sets could not be comparable for the differences in population size and since AFLP FST is a multilocus estimate while mtDNA FST takes into account explicitly sequence divergence. Nevertheless, the significant genetic differentiation of TZ vs. WZ and TZ vs. ND observed in AFLP marker was not identified by mtDNA. Likely, AFLP marker showed a higher performance than the CR in detecting significant genetic differences within D. akajei, which was also revealed by previous study on the Eastern Atlantic skates (Pasolini et al., 2011). In accordance with the results of mtDNA, the geographic distance could not explain genetic divergence among populations by AFLP. D. akajei experienced population expansion during Pleistocene period, followed by the rebuilt of genetic structure. The weak but significant genetic structure suggested that the D. akajei populations may be at the progress to attain the migration–drift equilibrium. The small but effective immigrants could be responsible for maintaining this genetic pattern. Identical to the result of CR-based analysis, no genetic differentiation was detectable between freshwater and marine populations, which probably contradicted the hypothesis that the freshwater D. akajei is a ‘land-lock’ population resulted from long-term separation from ocean, although small sample size tempered this. More samples from the South China Sea need to be analyzed to test the hypothesis. Towards marine populations, however, this genetic pattern of significant genetic structuring within oceanic basin and along certain continuous coastline has been increasingly reported in many highly mobile sharks circumglobally distributed (Keeney and Heist, 2006; Chapman et al., 2009), as well as in less mobile benthic species Table 4 Pairwise FST (below diagonal) and gene flow (Nm) (above diagonal) between the four marine populations of D. akajei based on AFLP data. All values marked ** indicated the FST values were significant (P < 0.01) after the sequential Bonferroni procedure. ZZ ZZ ND TZ WZ
0.077** 0.115** 0.090**
ND
TZ
WZ
2.976
1.918 2.416
2.530 2.650 5.193
0.094** 0.086**
0.046**
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Table 5 Genetic structuring of D. akajei populations through AMOVA analysis. Structure tested
1. One gene pool (TZ, WZ, ND, ZZ) Among populations Within populations 2. Two gene pools (TZ, WZ, ND, FZ, ZZ, Ma) (CZ) Among groups Among populations within groups Within populations
Observed partition Variance
% Total
F Statistics
P
1.7 18.5
8.5 91.5
FST ¼ 0.085
0.00 0.00
1.2 2.2 18.4
5.6 10.0 84.4
FCT ¼ 0.056 FSC ¼ 0.105 FST ¼ 0.156
0.27 0.02 0.00 0.00 0.00 0.00
within a continuous distribution (i.e. Rbinobatus produtus, Sandoval-Castillo et al., 2004). Populations of the thornback ray, Raja clavata, in Mediterranean have drifted to a weak but detectable level of genetic differentiation with significant AFLP FST (0.035, P < 0.0001) (Pasolini et al., 2011). Significant allele-frequency differences were also detected in the benthic California angel shark (S. californica) around the California Channel Islands, over extremely short geographical distances (Gaida, 1997). As with many aplacental viviparous species, the dispersal of D. akajei is exclusively accomplished by adults. The behavior and vagility of adults undoubtedly influence the gene connectivity and differentiation among populations. In D. akajei, populations separated by physical barriers are unexpected since there is an apparent absence of effective environmental barrier against dispersal along the continuous coastline of the East China Sea. Thus, an efficient life-history functional barrier could play a role in the restriction to successful gene flow and in the genetic structuring. Many studies of coastal elasmobranch species have highlighted the importance of fine-scale habitat preferences driven by factors of life-span characteristics, such as reproductive requirement and behavioral philopatry (fidelity to mating, pupping, or feeding sites, Hueter et al., 2005) in genetic heterogeneity of populations (the short-tailed stingray, Dasyatis brevicaudata, Le Port and Lavery, 2012; the blacktip sharks, Carcharhinus limbatus, Keeney et al., 2003, 2005; Keeney and Heist, 2006). Therefore, the philopatric behavior would be an explanation for the unexpected population structure detected in many elasmobranches surveyed to date (e.g. the scalloped hammerhead sharks, Sphyrna lewini, Duncan et al., 2006). With respect to inferring the relative role of philopatric behavior in shaping and maintaining the patterns of population structure of D. akajei, our study was limited for a lack of the basic life-history strategies such as movement patterns and behavioral attributes (e.g. philopatric behavior) coupled with the environmental factors. Additionally, dispersal among populations of D. akajei might also be interrupted by other behavior restrictions that are probably linked to the low mobility of D. akajei individuals and to specific habitat requirements (e.g. demersal habit). Even though some effective migration occur sporadically among sites (1.9 < Nm < 5.2), different populations connected by a few migrants per generation can still be isolated in terms of biomass (Waples, 1998). Hence, regional or population-specific conservation management units should be implemented to minimize the resource decline.
Fig. 3. Plot of pairwise estimates of FST/(1 FST) and geographic distance between samples from four marine populations of D. akajei.
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Acknowledgments We thank Dr. Xiao Chen and Dr. Koji Yokogawa for collecting the samples. We are grateful to Ms. Yuan Deng for technical assistance. This work was funded by National Natural Science Foundation (41006080), the Fundamental Research Funds for the Central Universities (201213014, 201262022) and Key Laboratory of Fishery Ecology and Environment, Guangdong Province (LFE-2011-16).
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Contents lists available at ScienceDirect
Biochemical Systematics and Ecology journal homepage: www.elsevier.com/locate/biochemsyseco
Genetic diversity and population structure of the red stingray, Dasyatis akajei inferred by AFLP marker Ning Li a, Na Song a, Guang-ping Cheng b, **, Tian-xiang Gao a, * a b
Institute of Evolution and Marine Biodiversity, Ocean University of China, Qingdao 266003, China College of Animal Science and Technology, Guangxi University, Nanning 530004, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 19 March 2013 Accepted 9 August 2013 Available online
To examine population genetic diversity and variation of the red stingray Dasyatis akajei, samples from 1 freshwater region and 6 coastal localities within its distribution range were analyzed by using amplified fragment length polymorphism (AFLP) technology. A total of 207 loci were identified by 4 primer combinations from 87 individuals, 174 of which were polymorphic (84.1%). A high level of Nei’s gene diversity was observed with the overall value of 0.230 0.179. No significant genealogical clusters associated with sampling sites were revealed on the UPGMA tree. Both analysis of molecular variance (AMOVA, FST ¼ 0.085, P ¼ 0.00) and pairwise FST indicated significant genetic differentiation among four marine samples. However, no particular genetic differentiation was detected between marine and the limited sampling freshwater populations (AMOVA, FCT ¼ 0.056, P > 0.05). Except for the TZ vs. WZ (5.193), the gene flow estimates (Nm) demonstrated the effective immigrants were 1.918-2.976, suggesting low level dispersal between pairwise marine populations. Species-specific habits (demersal and sluggish habits) are probably responsible for the population structuring. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Dasyatis akajei AFLP marker Genetic diversity Genetic structure
1. Introduction The red stingray, Dasyatis akajei, is a typical demersal elasmobranch in the family Dasyatidae and is distributed coastally along the northwestern Pacific Ocean, from Japan to China (Nishida and Nakaya, 1990; Huveneers and Ishihara, 2006), even presumably occurred as far as Thailand, the Philippines, Fiji and Tuvalu (FishBase, 2009). D. akajei can inhabit not only in coastal area but in some rivers of Guangxi Province of China (Lin and Zeng, 1987), where the freshwater populations are deemed to be ‘land-locked’. Characterized by high longevity and low fecundity, D. akajei is considered highly vulnerable to habitat destruction and over-exploration. The red stingray has been listed as ‘Near Threatened’ species on the IUCN Red List (Huveneers and Ishihara, 2006) and its freshwater population is also defined as National Class II Protected Animals of China. As predominantly live-bears, the dispersal of rays is not dependent on the release of pelagic eggs and larvae, but determined by the vagility of adults. For coastal species of elasmobranch, the dispersal may be limited by their complex interactions with coastal environments (such as use of nursery areas) (Simpfendorfer and Milward, 1993). Accumulating evidence has suggested that the species-specific behavior, such as habitat preferences and/or site fidelity, is operated as the potential isolation factor in shaping the pattern of genetic structure of elasmobranch along continuous coastline (Keeney
* Corresponding author. Tel.: þ86 532 8203 2063; fax: þ86 532 8203 2076. ** Corresponding author. Tel.: þ86 771 323 5256. E-mail addresses: [email protected] (G.-p. Cheng), [email protected] (T.-x. Gao). 0305-1978/$ – see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.bse.2013.08.009
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et al., 2005; Le Port and Lavery, 2012). Population genetic differentiation over smallest scale has been demonstrated in the Pacific angel shark Squatina californica, which might be attributed to a combination of behavioral characteristics and topography of the sea floor (Gaida, 1997). With benthic and sluggish habits, the gene flow by dispersal among D. akajei populations might be not expected. Thus, further research is needed to explain the genetic variations among populations. Detecting genetic variation and population structuring of species has attracted much attention, since the genetic composition is usable for the assessment of genetic diversity of natural populations (Keiper and McConchie, 2000). It is suggested that population heterozygosity levels are linked directly to evolutionary potential and population fitness, and need to be kept to minimize their risk of decline or extinction (Reed and Frankham, 2003). Further, the accurate delimitation of species genetic diversity and population structure is in an important resource recovery need and can provide the essential baseline information that is desirable for appropriate fisheries management and conservation (Beheregaray, 2008). For instance, genetic investigations aided the resurrection of the maskray genus Neotrygon for a group of Indo-Pacific dasyatids which displayed considerable mitochondrial DNA (mtDNA) genetic diversity (Last et al., 2010). Previous studies of D. akajei only focused on the histology and biochemistry, and no attention to genetic investigations have been paid till now. On account of the D. akajei resource in a crisis, understanding its genetic diversity and population structure is a high priority for conservation of biodiversity and sustainable long-term management. Nuclear markers, focusing on hyper-variable regions of the genome, offer increased resolving power for the genetic polymorphism and population structure missed by mtDNA haplotypes on a finer scale (Bazin et al., 2006; Dudgeon et al., 2012). Among the several nuclear marker systems, AFLP (Vos et al., 1995) is highly efficient for the assessment of genetic variation pattern among and within populations (Travis et al., 1996). As a multi-locus fingerprinting technique, AFLP does not need previously known genetic information, a feature especially useful for those species for which both polymorphic markers in relation to population identity and sequence information are limited (Ajmone-Marsan et al., 2001). Up to now, AFLP marker has been applied to detect the genetic diversity and genetic structuring pattern in a growing body of marine organisms (Lerceteau and Szmidt, 1999; Mickett et al., 2003; Liu et al., 2009). In the present study, we presented a population genetic survey for both freshwater and marine populations of D. akajei using AFLP technique. Our aim was to investigate genetic diversity and characterize population structuring of D. akajei, and further to provide potential implications for the effective fishery management and conservative strategies. 2. Materials and methods 2.1. Sample collection Eighty-seven samples from 7 localities were collected within the distribution area of D. akajei from 2004 to 2008. Among them, four individuals were sampled from the Longzhou in River Guangxi of China. The remainders were collected along the coast of China but included a population recovered from Maizuru in Japan (Fig. 1). All individuals were defined morphologically. A fraction of caudal fin were excised and preserved in 95% ethanol for DNA extraction. Moreover, one specimen from its congener Dasyatis bennetti and two from Dasyatis izuensis, were also collected to serve as outgroups for phylogenetic analysis. 2.2. Genomic DNA extraction and AFLP method Total genomic DNA was isolated from muscle tissue by proteinase K digestion followed by a standard phenol-chloroform method. Procedures of AFLP were essentially based on Vos et al. (1995) and Wang et al. (2000). About 100 ng genomic DNA was digested with 1 unit of EcoR I and Mse I (NEB) at 37 C for 6 h. Double-stranded adapters were ligated to the restriction fragments at 20 C overnight after adding 1 mL 10 ligation buffer, 5 pmol EcoR I adapter (EcoR I-1/EcoR I-2; Table 1), 50 pmol Mse I adapter (Mse I-1/Mse I-2; Table 1), 0.3 unit of T4 DNA ligase (Promega) with a final volume of 10 mL. Preamplification PCR reaction was conducted using an Eppendorf Thermocycler (Mastercycler 5334) with a pair of primers containing a single selective nucleotide. Amplification was performed at an annealing temperature of 53 C for 30 s. The 20 mL PCR product mixture was diluted 10-fold with distilled water and used as templates for the subsequent selective PCR amplification. The selective amplifications were carried out in 20 mL PCR reaction volume containing 1 mL productions of preamplifications, 1 PCR reaction buffer, 150 mM of each dNTP, 30 ng of each selective primer, and 0.5 unit of Taq DNA polymerase on a gradient thermal cycler (Mastercycler 5334) with a touchdown cycling profile of nine cycles of 30 s at 94 C, 30 s at 65 C (1 C at each cycle), and 30 s at 72 C followed by the cycling profile of 28 cycles of 30 s at 94 C, 30 s at 56 C, and 1 min at 72 C. The final step was a prolonged extension of 7 min at 72 C. PCR products were run on 6.0% denaturing polyacrylamide gel electrophoresis (PAGE) for 2.5 h at 50 C on the Sequi-Gen GT Sequencing Cell (Bio-Rad, USA), and finally detected using the silver staining technique modified from Merril et al. (1979). Sequences of AFLP adapters and primers are listed in Table 1. Four primer combinations (E-ACG/M-CAA, E-ACG/M-CAG, E-ACG/M-CTA, E-ACG/M-CTC) were chosen for AFLP analysis (Table 1). 2.3. Data analysis Excluding smeared and weak bands by visual inspection, clear and unambiguous AFLP bands were recorded for binary data (1 ¼ presence, 0 ¼ absence), and a matrix was generated based on the binary characters. To estimate the genetic diversity of D. akajei, POPGENE software (Yeh et al., 1999) was used to calculate the number of polymorphic loci, proportion of
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Fig. 1. Sampling sites of D. akajei. C ¼ sampling sites for marine populations and : ¼ sampling sites for the freshwater population.
polymorphic loci and Nei’s genetic diversity. Similarity indices were calculated using the formula S ¼ 2Nab/(Na þ Nb) (Nei and Li, 1979), where Na and Nb are the number of bands in individuals a and b, respectively and Nab is the number of sharing bands. Genetic distances between individuals were computed using the formula D ¼ ln S (Nei and Li, 1979). Genetic relationships among individuals were estimated by constructing UPGMA tree based on Nei’s genetic distance (Nei, 1978) in Mega 4.0 (Kumar et al., 2008). To evaluate the genetic differentiation among populations (limited sampling populations were not analyzed), pairwise fixation index FST was calculated as implemented in ARLEQUIN (Schneider et al., 2000). Significance of pairwise FST comparisons was tested by 10,000 permutations and subsequently adjusted by sequential Bonferroni correction (Rice, 1989). Analysis of molecular variance (AMOVA) (Excoffier et al., 1992) was employed to further test the significant population structure as well as the geographical pattern of population subdivision (Excoffier et al., 1992). The gene flows were estimated by the equation: Nm¼(1 FST)/4FST (Wright, 1951), where Nm is the number of effective immigrants per generation.
Table 1 Adaptors and primer combinations sequences used in the study. Primer Adapters
Sequence EcoR I-adapter Mse I-adapter
Pre-amplification primer Selective amplification primer
EcoR I Mse I E-ACG/M-CAA E-ACG/M-CAG E-ACG/M-CTA E-ACG/M-CTC
50 -CTCGTAGACTGCGTACC-30 50 -AATTGGTACGCAGTCTAC-30 50 -GACGTGAGTCCTGAG-30 50 -TACTCAGGACTCAT-30 50 -GACTGCGTACCAATTC-30 50 -GATGAGTCCTGAGTAA-30 50 -GACTGCGTACCAATTCACG-30 50 -GATGAGTCCTGAGTAACAA-30 50 -GACTGCGTACCAATTCACG-30 50 -GATGAGTCCTGAGTAACAG-30 50 -GACTGCGTACCAATTCACG-30 50 -GATGAGTCCTGAGTAACTA-30 50 -GACTGCGTACCAATTCACG-30 50 -GATGAGTCCTGAGTAACTC-30
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Table 2 Number of bands generated by four primer combinations.
Number of loci Number of polymorphic loci Proportion of polymorphic loci
M-CAA/E-ACG
M-CAG/E-ACG
M-CTA/E-ACG
M-CTC/E-ACG
Total
40 32 80.0%
41 32 78.1%
64 59 92.2%
62 47 75.8%
207 174 84.1%
To examine the isolation by distance (Slatkin, 1993), pairwise values of FST/(1 FST) were plotted against geographical distance (one-dimensional stepping-stone model) between four marine populations (TZ, WZ, ND and ZZ). The strength and significance of the relationship between genetic distances and geographic distances was assessed using reduced major axis (RMA) regression and Mantel tests using IBDWS (Isolation by distance web service at http://phage.sdsu.edu/wjensen/) (Bohonak, 2002; Jensen et al., 2005). 3. Results In eighty-seven individuals from seven populations, a total number of 207 bands were identified by four AFLP primer combinations, of which 174 bands were polymorphic (84.1%) (Table 2). For each primer pair combined, the number of polymorphic loci scored ranged from 32 to 59 with the spectrum of proportion of polymorphic loci from 75.8% to 92.2% (Table 2). Across all individuals, the highest average genetic similarity but the lowest average genetic distance (0.131) were indicated by TZ population (0.877), while the contrary by Ma population (0.188/0.829). Of all the seven populations, the genetic diversity estimation for FZ, CZ and Ma populations, small in size, may be of no comparability with others and would not be further considered. In term of the four marine populations (TZ, WZ, ND and ZZ), ND population showed the greatest percent of polymorphic loci (68.9%), and the lowest was represented by TZ population (64.2%). Assuming Hardy–Weinberg equilibrium, ZZ and TZ populations possessed the highest (0.254 0.209) and the lowest gene diversity (0.198 0.195) respectively (Table 3). UPGMA tree rooted with D. bennetti and D. izuensis was constructed based on Nei’s genetic distance (Fig. 2). Within the D. akajei populations, there appeared to be no significant genealogical clustering with correspondence to sampling localities overall, except for several geographical subclades. Based on one-gene pool that grouped the four marine populations (TZ, WZ, ND and ZZ) for AMOVA, the result indicated genetic variation primarily existed within populations (91.5%) and 8.5% among populations. A weak but significant genetic structure among populations was detected by AMOVA (FST ¼ 0.085, P ¼ 0.00) as revealed by pairwise FST values. The greatest magnitude of genetic differentiation occurred among the geographically distant populations TZ vs. ZZ (FST ¼ 0.115, P < 0.01), meanwhile the lowest between adjacent populations TZ vs. WZ (FST ¼ 0.046, P < 0.01) (Table 4). Comparably, pairwise gene flow estimates (Nm) among populations varied from 1.918 (TZ vs. ZZ) to 5.193 (TZ vs. WZ). No genetic differentiation (P > 0.05) was detected between marine and freshwater populations by two gene pools-based analysis (AMOVA, FCT ¼ 0.056, P > 0.05) (Table 5). Moreover, Mantel test showed no significant correlation of genetic divergence with geographical distance (r ¼ 0.693, P > 0.05) (Fig. 3). 4. Discussion Estimates of genetic diversity provide a measure of an organism’s adaptive potential and survivability in respond to environmental change (Ryman et al., 1995; Schmitt and Hewitt, 2004). Based on the AFLP marker, investigation on genetic diversity of D. akajei populations presented a high level of Nei’s index and the proportion of polymorphic loci in comparison with the current reported studies on elasmobranches (Pasolini et al., 2011). The result was compatible with the mtDNA-based estimates of a high level of haplotype diversity, which appeared to be a rare phenomenon for a species undergoing resource declining. However, recessive populations seem to retain historic levels of genetic diversity if the decline has occurred recently (Roman and Palumbi, 2003; Bowen et al., 2007). As an incidental catch of commercial fisheries, no direct resource investigation was carried out for D. akajei, of which the unoptimisticable status in genetic diversity implied the conservation of this species can not be eased. Table 3 Sampling information of D. akajei including sampling sites, date of collection, sample size and several genetic diversity indices. ID
Population
Sampling date
Sample size
Number of loci
Number of polymorphic loci
Proportion of polymorphic loci
Average genetic similarity/average genetic distance
Nei’s gene diversity
TZ WZ ND FZ ZZ CZ Ma
Taizhou, Jiaojiang, China Wenzhou, Dongtou, China Ningde, Xiapu, China Fuzhou, Pingtan, China Zhangzhou, Dongshan, China Chongzuo, Longzhou, China Maizuru, Japan
October 2008 April 2009 October 2008 October 2008 October 2008 August 2004 April 2005
19 17 22 3 16 5 5
182 193 187 199 157 147 161
120 137 133 50 125 67 85
64.2% 68.8% 68.9% 31.9% 68.7% 45.6% 52.8%
0.877/0.131 0.851/0.161 0.870/0.139 0.873/0.136 0.830/0.187 0.865/0.145 0.829/0.188
0.198 0.225 0.220 0.122 0.254 0.179 0.192
0.195 0.198 0.193 0.187 0.209 0.214 0.205
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Fig. 2. UPGMA cluster analysis based on Nei’s genetic distances for seven populations of D. akajei. D. bennetti and D. izuensis were selected as outgroups to root the tree, and bootstrap support values were shown besides the internal branches.
Most genetic analyses of continuously distributed coastal species exhibit a trend toward high dispersal and low level of genetic structure (Graves, 1998; Duncan et al., 2006). Extraordinarily, D. akajei populations displayed a weak but significant genetic structure across short costal distances, as evidenced by pairwise FST and Nm between four marine populations and further by AMOVA analysis, supporting limited gene flow among populations of D. akajei. The significant population structuring was also evident as indicated by mtDNA-based indices (pairwise FST and AMOVA FST, Li et al., unpublished data), which were higher than the corresponding values calculated using AFLP data. Likely, the indices between these two marker sets could not be comparable for the differences in population size and since AFLP FST is a multilocus estimate while mtDNA FST takes into account explicitly sequence divergence. Nevertheless, the significant genetic differentiation of TZ vs. WZ and TZ vs. ND observed in AFLP marker was not identified by mtDNA. Likely, AFLP marker showed a higher performance than the CR in detecting significant genetic differences within D. akajei, which was also revealed by previous study on the Eastern Atlantic skates (Pasolini et al., 2011). In accordance with the results of mtDNA, the geographic distance could not explain genetic divergence among populations by AFLP. D. akajei experienced population expansion during Pleistocene period, followed by the rebuilt of genetic structure. The weak but significant genetic structure suggested that the D. akajei populations may be at the progress to attain the migration–drift equilibrium. The small but effective immigrants could be responsible for maintaining this genetic pattern. Identical to the result of CR-based analysis, no genetic differentiation was detectable between freshwater and marine populations, which probably contradicted the hypothesis that the freshwater D. akajei is a ‘land-lock’ population resulted from long-term separation from ocean, although small sample size tempered this. More samples from the South China Sea need to be analyzed to test the hypothesis. Towards marine populations, however, this genetic pattern of significant genetic structuring within oceanic basin and along certain continuous coastline has been increasingly reported in many highly mobile sharks circumglobally distributed (Keeney and Heist, 2006; Chapman et al., 2009), as well as in less mobile benthic species Table 4 Pairwise FST (below diagonal) and gene flow (Nm) (above diagonal) between the four marine populations of D. akajei based on AFLP data. All values marked ** indicated the FST values were significant (P < 0.01) after the sequential Bonferroni procedure. ZZ ZZ ND TZ WZ
0.077** 0.115** 0.090**
ND
TZ
WZ
2.976
1.918 2.416
2.530 2.650 5.193
0.094** 0.086**
0.046**
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Table 5 Genetic structuring of D. akajei populations through AMOVA analysis. Structure tested
1. One gene pool (TZ, WZ, ND, ZZ) Among populations Within populations 2. Two gene pools (TZ, WZ, ND, FZ, ZZ, Ma) (CZ) Among groups Among populations within groups Within populations
Observed partition Variance
% Total
F Statistics
P
1.7 18.5
8.5 91.5
FST ¼ 0.085
0.00 0.00
1.2 2.2 18.4
5.6 10.0 84.4
FCT ¼ 0.056 FSC ¼ 0.105 FST ¼ 0.156
0.27 0.02 0.00 0.00 0.00 0.00
within a continuous distribution (i.e. Rbinobatus produtus, Sandoval-Castillo et al., 2004). Populations of the thornback ray, Raja clavata, in Mediterranean have drifted to a weak but detectable level of genetic differentiation with significant AFLP FST (0.035, P < 0.0001) (Pasolini et al., 2011). Significant allele-frequency differences were also detected in the benthic California angel shark (S. californica) around the California Channel Islands, over extremely short geographical distances (Gaida, 1997). As with many aplacental viviparous species, the dispersal of D. akajei is exclusively accomplished by adults. The behavior and vagility of adults undoubtedly influence the gene connectivity and differentiation among populations. In D. akajei, populations separated by physical barriers are unexpected since there is an apparent absence of effective environmental barrier against dispersal along the continuous coastline of the East China Sea. Thus, an efficient life-history functional barrier could play a role in the restriction to successful gene flow and in the genetic structuring. Many studies of coastal elasmobranch species have highlighted the importance of fine-scale habitat preferences driven by factors of life-span characteristics, such as reproductive requirement and behavioral philopatry (fidelity to mating, pupping, or feeding sites, Hueter et al., 2005) in genetic heterogeneity of populations (the short-tailed stingray, Dasyatis brevicaudata, Le Port and Lavery, 2012; the blacktip sharks, Carcharhinus limbatus, Keeney et al., 2003, 2005; Keeney and Heist, 2006). Therefore, the philopatric behavior would be an explanation for the unexpected population structure detected in many elasmobranches surveyed to date (e.g. the scalloped hammerhead sharks, Sphyrna lewini, Duncan et al., 2006). With respect to inferring the relative role of philopatric behavior in shaping and maintaining the patterns of population structure of D. akajei, our study was limited for a lack of the basic life-history strategies such as movement patterns and behavioral attributes (e.g. philopatric behavior) coupled with the environmental factors. Additionally, dispersal among populations of D. akajei might also be interrupted by other behavior restrictions that are probably linked to the low mobility of D. akajei individuals and to specific habitat requirements (e.g. demersal habit). Even though some effective migration occur sporadically among sites (1.9 < Nm < 5.2), different populations connected by a few migrants per generation can still be isolated in terms of biomass (Waples, 1998). Hence, regional or population-specific conservation management units should be implemented to minimize the resource decline.
Fig. 3. Plot of pairwise estimates of FST/(1 FST) and geographic distance between samples from four marine populations of D. akajei.
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Acknowledgments We thank Dr. Xiao Chen and Dr. Koji Yokogawa for collecting the samples. We are grateful to Ms. Yuan Deng for technical assistance. This work was funded by National Natural Science Foundation (41006080), the Fundamental Research Funds for the Central Universities (201213014, 201262022) and Key Laboratory of Fishery Ecology and Environment, Guangdong Province (LFE-2011-16).
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