Deep gene exchange break among Konosirus punctatus populations across the Northwestern Pacific inferred from AFLP and ISSR markers
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Abstract: The correct understanding of fish population structure plays a positive role in their fishery management. The dotted gizzard shad, Konosirus punctatus, is widely distributed in the coastal waters of the Northwestern Pacific. With the over-exploitation of economically important fishes, its importance is increasingly prominent. To further examine the population genetic structure of K. punctatus across the Northwestern Pacific, the amplified fragment length polymorphism (AFLP) and the inter-simple sequence repeats (ISSRs) were employed to perform genetic variation analysis. The results showed that the combination of polyacrylamide gel electrophoresis and silver staining can effectively detect genetic variation for K. punctatus populations. The average proportions of polymorphic loci were 46.26% and 87.13% for AFLP and ISSR markers, respectively, and the genetic diversity parameters showed no obvious differences among populations. Both AMOVA and pairwise Fst suggested that there was significant genetic differentiation between Chinese and Japanese populations. All samples also clustered into two clades based on the UPGMA tree by two markers, which indicated significant genetic differentiation among populations. Consistent with the previous studies, there are two highly differentiated groups at the nuclear gene level and they were suggested to be treated as two separate genetic management units. The results of the present study could provide the genetic management strategy for this important economic species.
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Key words:
- Konosirus punctatus /
- population genetic differentiation /
- AFLP /
- ISSR
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1. Introduction
The dotted gizzard shad, Konosirus punctatus, belongs to the family Clupeidae, Clupeiformes, which is widely distributed in the coastal waters of the Northwestern Pacific (Whitehead, 1985; Zhang, 2001). It is a euryhalinous species and can tolerate a wide range of salinity, even surviving in freshwater (Kuroda et al., 2002). Konosirus punctatus has commercial value and many studies focused on its growth, feeding habit, and biology (Kuroda et al., 2002; Kawasaki et al., 2006; Choi et al., 2015; Ping et al., 2019). The larval pelagic duration of K. punctatus is about 24−28 d, and the adults of K. punctatus mainly feed on plankton and organic debris, which makes it to be an excellent mixed-culture species for aquaculture (Song et al., 2017; Shan et al., 2020a). With the over-exploitation of economically important fishes, the proportion of small pelagic fishes gradually increased (Bian et al., 2022). However, although K. punctatus has been reported to be the dominant species in some coastal waters (Zhang et al., 2022), the total market landings showed an obvious decline trend. The global capture production of K. punctatus declined from
23707 tonnes in 1995 to4300 tonnes in 2016 (Liu et al., 2020). Moreover, K. punctatus is constantly taken as by-catch, putting it at the crisis of overfishing. Since the 1960s, harbor breeding has started in the coastal areas of China (Ping et al., 2024). With the development of artificial breeding technology, the stock enhancement of K. punctatus was conducted to repair the marine ecosystem due to its low nutritional filter feeding habits (Shan et al., 2020b). Thus, K. punctatus is not only an important fishing object but also plays an important role in aquaculture and marine ecosystems.The correct understanding of fish population structure could provide an important reference basis for the evolutionary mechanism, and also play a positive role in their fishery management (Ying et al., 2011). Although marine fish were considered to lack obvious genetic differentiation due to the absence of obvious geographical isolation in the marine environment (Grant and Bowen, 1998; Zhang et al., 2020a), significant population genetic structure has been detected in many marine fish with the development of molecular biotechnology (Liu et al., 2007; Song et al., 2019). Until now there were several population genetic studies of K. punctatus (Myoung and Kim, 2014; Gwak et al., 2015; Li et al., 2016; Song et al., 2017; Liu et al., 2020), and its complete mitochondrial genome, the whole transcriptome, and the chromosome-level genome were also reported (Li et al., 2016; Zhang et al., 2020b; Lou et al., 2021; Liu et al., 2022). We used the first hypervariable region of mitochondrial DNA marker to evaluate the population genetic divergence of K. punctatus across the Northwestern Pacific, and significant genetic differentiation was detected between Chinese and Japanese clades (Song et al., 2017). Moreover, there existed low genetic differentiation among populations of K. punctatus along the Chinese coast, which was also supported by another study using COI and Cyt b genes as molecular makers (Liu et al., 2020). The strong dispersal ability of the larvae and adults may play vital roles in gene homogeneity and the ocean currents may greatly promote the gene exchange of K. punctatus populations (Song et al., 2017; Liu et al., 2020). However, until now no population genetic studies based on nuclear markers have been conducted.
The amplified fragment length polymorphism (AFLP), is a multi-locus fingerprinting technique, which has the advantages of high repeatability and polymorphic sites (Vos et al., 1995). It combines the advantages of RFLP and RAPD and overcomes their instability and constraints, and has been widely used in studies on the population genetics of marine organisms (Ferreira et al., 2015; Da Silva et al., 2016). The inter-simple sequence repeats (ISSRs), are also powerful molecular tools for population genetic studies, which have the advantages of being fast, stable, low cost and does not require a priori genome sequence information (Yang et al., 2011; Kamangar and Rostamzadeh, 2015). Although the simple sequence repeats (SSRs) are more commonly used in population genetic studies, they often do not transfer well from one species to another, and we often need to isolate and develop new microsatellite primers for each taxon (Castoe et al., 2010). And then compared with SSRs, ISSRs could be amplified without knowing the sequence information in advance and have better versatility.
In this study, the population genetic structure of K. punctatus was firstly evaluated by the combination of AFLP and ISSRs. We want to check the genetic variations from the perspective of nuclear gene level, and provide the genetic management strategy for this important economic species.
2. Materials and methods
2.1 Sample collection
Samples of K. punctatus were collected from nine localities along the Chinese and Japanese coasts between 2006 and 2007 (Table 1, Fig. 1). Six populations were collected from Chinese coastal waters and three populations were collected from Japan. All individuals were identified on the basis of morphology (Zhang, 2001), and a piece of muscle was preserved in 95% ethanol. All nine populations were used in the AFLP analysis and five of them (QD, ZS, DB, AM and NG) were chosen for ISSR analysis (Table 1).
Table 1. Sample information and genetic diversity information of K. punctatusPopulations ID Date of
collectionn Number
of lociNumber of
polymorphic
lociProportion of
polymorphic
loci/%Nei’s genetic
diversityShannon’s
diversity indexAFLP Aomori AM 2006-05 17 192 62 32.29 0.091 8 0.140 1 Tokyo Bay TB 2007-06 27 196 67 34.18 0.080 1 0.126 8 Nagasaki NG 2007-06 27 191 66 34.55 0.077 1 0.123 1 Qingdao QD 2006-04 17 198 70 35.35 0.079 7 0.128 6 Yellow River
EstuaryYR 2007-04 14 195 62 31.79 0.069 5 0.112 8 Zhoushan ZS 2006-05 14 196 65 33.16 0.091 8 0.141 9 Chengshantou CS 2007-05 19 196 74 37.75 0.089 1 0.142 0 Kongdong Island KI 2007-05 16 195 62 31.79 0.061 7 0.102 4 Daya Bay DB 2006-04 15 182 50 27.47 0.062 6 0.099 7 Total 166 214 99 46.26 ISSR Aomori AM 2006-05 18 168 120 71.42 0.179 1 0.277 Nagasaki NG 2007-06 17 167 126 75.44 0.191 1 0.292 3 Qingdao QD 2006-04 18 168 128 76.19 0.182 3 0.284 Zhoushan ZS 2006-05 17 163 118 72.39 0.182 7 0.280 8 Daya Bay DB 2006-04 18 165 127 76.96 0.184 5 0.287 7 Total 202 176 87.13 | Show Table
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Figure 1. Sample locations of K. punctatus in the present study.Genomic DNA was isolated from muscle by proteinase K digestion followed by a standard phenol–chloroform method (Sambrock and Russel, 2001). The procedures of AFLP were essentially based on Vos et al. (1995) and Wang et al. (2000). Five selective primer combinations were tested and used in the present study, including E-ACC/M-CTA, E-AGA/M-CTG, E-AGG/M-CTT, E-ACG/M-CTC, and E-AGA/M-CTA. Procedures of ISSRs were essentially the same of Yang et al. (2011). ISSR primers used in this study were according to the primer set published by Yang et al. (2008) and University of British Columbia (UBC) (
http://www.michaelsmith.ubc.ca/services/NAPS/Primer_Sets/Primers_Oct2006.pdf ). A total of 12 ISSR primers were assessed, and four highly polymorphic primers (UBC834, ISSR4, UBC841, and ISSR62) were chosen for the following analysis.All the PCR products of AFLP and ISSR 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).
2.2 Data analysis
All clear and unambiguous AFLP and ISSR bands were recorded as binary data (1=presence, 0=absence), and transformed into a 0/1 binary character matrix in Microsoft Office Excel 2010. POPGENE 1.3.1 software was used to calculate the genetic diversity index such as the percentages of polymorphic loci, Nei’s genetic diversity and Shannon diversity (Nei and Li, 1979). The UPGMA (unweighted pair-group method analysis) tree of individuals was constructed by software MEGA7.0 based on Nei’s genetic distance (Kumar et al., 2016). ARLEQUIN 3.5 was used to calculate pairwise fixation index Fst between pairs of population samples and the significance of the Fst was tested by
10000 permutations for each pairwise comparison (Excoffier and Lischer, 2010). When multiple comparisons were performed, P values were adjusted using the sequential Bonferroni procedure (Rice, 1989). To further examine hierarchical population structure as well as the geographical pattern of population subdivision, we used analysis of molecular variance (AMOVA) (Excoffier et al., 1992). Chinese group (population QD, ZS, YR, KI, CS and DB) and Japanese group (population AM, TB and NG) were defined for AMOVA and each group was also analyzed separately.3. Results
3.1 Population genetic diversity
A total of 214 clear and unambiguous bands were amplified by 5 AFLP selective primers for 166 K. punctatus individuals (Table 1). The total number of polymorphic loci was 99 (46.26%) and varied for each primer combination (12-21) (Table 2). The number of polymorphic loci per population ranged from 50 (DB) to 74 (CS), and the corresponding percentage was 27.47% to 37.75%. Population ZS and AM showed the highest Nei’s genetic diversity and population CS showed the highest Shannon’s diversity index, while population KI showed the lowest Nei’s genetic diversity and population DB showed the lowest Shannon’s diversity index (Table 1).
Table 2. Polymorphism information of primers based on two markersAFLP primers E-ACC/
M-CTAE-AGA/
M-CTGE-AGG/
M-CTTE-ACG/
M-CTCE-AGA/
M-CTATotal Number of loci 42 71 39 23 39 214 Polymorphic loci 19 31 17 12 20 99 Proportion of polymorphic loci/% 45.23 43.66 43.59 52.17 51.28 46.26 ISSR primers UBC834 ISSR4 UBC841 ISSR62 Total Number of loci 41 58 62 41 \ 202 Noumber of polymorphic loci 39 54 50 33 \ 176 Proportion of polymorphic loci/% 95.12 93.10 80.65 80.49 \ 87.13 | Show TableDownLoad:
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A total of 202 clear and unambiguous bands were amplified by 4 ISSR primers for 88 K. punctatus individuals (Table 1). The total number of polymorphic loci was 176 (87.13%) and varied for each primer combination (33-54) (Table 2). The number of polymorphic loci per population ranged from 118 (ZS) to 128 (QD). Population AM showed the lowest Nei’s genetic diversity and Shannon’s diversity index, and population CS showed the highest Shannon’s information index, while population NG showed the highest Nei’s genetic diversity and Shannon’s diversity index (Table 1).
3.2 Population genetic structure
Two distinct clades were identified for K. punctatus based on Nei’s genetic distance among 166 individuals by AFLP markers (Fig. 2). Individuals from Chinese populations clustered together and the Japanese clade contained all the individuals from three Japanese populations. Samples also clustered into two clades based on UPGMA tree by ISSR markers, which indicated significant genetic differentiation among Chinese and Japanese populations (Fig. 2).
Figure 2. UPGMA tree based on genetic distance among individuals of K. punctatus by AFLP (left) and ISSR (right) markers.The analysis of pairwise Fst values among populations showed that there was significant genetic differentiation between Chinese and Japanese populations (Tables 3 and 4). To detect the variance components, the AMOVA analysis was conducted by two gene pools (Chinese group and Japanese group). The results showed that differentiation between the two groups was very strong and statistically significant (Fst=0.364 3, P<0.01) (Table 5). In the Japanese group, 98.37% of the genetic variation existed within populations. The genetic differentiation between population TB and NG was weak, while population AM showed significant genetic difference with these two populations. In the Chinese group, 98.71% of the genetic variation existed within populations. Most pairwise Fst values were low and not significant after sequential Bonferroni correction except the comparisons between population DB with other populations (Table 5). The AMOVA analysis by ISSR markers also revealed significant genetic differentiation between the two groups, which suggested limited gene flow among these populations. By contrast, the genetic differentiation between population AM and NG was low and not significant.
Table 3. Pairwise Fst values (below) and genetic distance (above) among K. punctatus populations by AFLP markerAM TB NG QD ZS YR KI CS DB AM 0.006 8 0.006 2 0.062 6 0.060 9 0.058 9 0.060 7 0.063 4 0.067 8 TB 0.034 49* 0.001 4 0.067 8 0.069 4 0.068 8 0.068 9 0.070 9 0.074 7 NG 0.032 46* −0.006 05 0.067 1 0.068 9 0.066 3 0.064 9 0.067 2 0.071 1 QD 0.337 90** 0.352 47** 0.361 87** 0.002 9 0.003 6 0.002 8 0.002 6 0.005 5 ZS 0.338 42** 0.362 06** 0.373 36** −0.008 73 0.003 2 0.004 8 0.005 5 0.008 YR 0.333 09** 0.362 11** 0.366 64** −0.001 66 −0.007 83 0.004 1 0.003 0.008 3 KI 0.351 59** 0.371 7** 0.370 26** −0.004 98 0.012 15 0.006 18 0.004 4 0.007 CS 0.351 59** 0.375 12** 0.374 37** −0.005 03 0.019 2* −0.003 76 0.013 26 0.004 8 DB 0.351 59** 0.413 14** 0.415 39** 0.0257 2*0.052 09* 0.0567 4*0.047 01* 0.022 2 Note: * Significant values after Bonferroni correct at 5% (P<0.05); ** significant values after Bonferroni correct at 1% (P<0.01). | Show TableDownLoad:
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Table 4. Pairwise Fst value (below) and genetic distance (above) between K. punctatus populations by ISSR markerAM NG QD ZS DB AM 0.005 6 0.105 6 0.109 6 0.127 9 NG −0.006 8 0.109 0.116 0.129 6 QD 0.284 66* 0.273 59* 0.004 7 0.014 9 ZS 0.291 24* 0.284 81* −0.01 0.015 7 DB 0.323 67* 0.307 88* 0.032 21* 0.034 13* Note: * Significant values after Bonferroni correct at 5% (P<0.05); ** significant values after Bonferroni correct at 1% (P<0.01). | Show TableDownLoad:
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Table 5. AMOVA of K. punctatus by AFLP and ISSR markersSource of variation AFLP ISSR Df Sum of squares Variance components Percentage of variation/% Df Sum of squares Variance components Percentage of variation/% All populations Among groups 1 492.437 5.898 97 36.42 1 368.878 8.188 28 36.42 Among populations within groups 7 88.605 0.139 72 0.86 3 71.015 0.212 04 0.86 Within populations 157 1 594.434 10.155 63 62.71 83 1 654.971 19.939 40 62.71 Total 165 2 175.476 16.194 02 87 2 094.864 28.339 73 Japanese group Among populations 2 29.287 0.175 5 1.63 1 18.024 −0.138 11 −0.68 Within populations 68 718.911 10.572 22 98.37 33 674.490 20.439 10 100.68 Total 70 748.197 10.747 71 34 692.514 20.300 98 Chinese group Among populations 5 59.319 0.128 31 1.29 2 52.991 0.389 91 1.95 Within populations 89 875.523 9.837 34 98.71 50 980.480 19.609 61 Total 94 934.842 9.965 65 52 1 033.472 19.999 52 | Show TableDownLoad:
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4. Discussion
4.1 Population genetic diversity
In the present study, each primer produced an average of 42.8 and 50.5 bands for AFLP and ISSR markers, respectively, which showed their high amplified efficiency. The combination of polyacrylamide gel electrophoresis and silver staining could greatly improve the detection rate of bands and the proportion of polymorphic sites, which was much higher than those by agarose electrophoresis (Yang et al., 2008; Liu et al., 2009). The average proportion of polymorphic loci was 46.26% and 87.13% for two markers, and high polymorphism can effectively detect genetic differentiation among populations. ISSR or AFLP markers were thought to be more suitable for estimating the genetic diversity of germplasm resources than SSR markers because of their higher marker indices (Sabir et al., 2014). Moreover, the proportion of polymorphic loci was higher in ISSR markers than in AFLP in this study. Meng and Chen (2001) found that ISSR markers were more effective and economical than AFLP markers in detecting genetic variation in Phialophora gregata, which was consistent with the results of this study.
The genetic diversity parameters for all populations, including the proportion of polymorphic loci, Nei’s and Shannon’s diversity indices, showed no obvious population differences. According to the previous results based on the mitochondrial DNA marker, Japanese populations exhibited higher nucleotide diversity than did Chinese populations (Song et al., 2017). Two subclades were detected in the Japanese clade, which may lead to high nucleotide diversity. The fluctuation of Pleistocene glacial climate led to the isolation and the secondary connection after glacial time may increase the nucleotide diversity (Fields et al., 2016). Therefore, higher nucleotide diversity for Japanese populations was detected by mitochondrial DNA marker.
4.2 Population genetic structure
The UPGMA tree based on Nei’s genetic distance showed that two distinct clades were detected for K. punctatus by two markers, which suggested strictly limited gene exchange between the Chinese and Japanese populations. The AMOVA and pairwise Fst analysis also confirmed this deduction. This phylogenetic pattern was highly consistent with previous studies by mitochondrial DNA (Song et al., 2017). The population genetic structure of marine organisms in the Northwestern Pacific had been proven to be strongly affected by the temperature changes of the Pleistocene ice age (Liu et al., 2007; Han et al., 2012). The drastic changes in sea level led to population contraction and expansion or range shifts, which can leave marks on the population’s genetic structure (Cheang et al., 2012; Yan et al., 2015). The survivals of K. punctatus in the different glacial refugia may re-colonize to the new habitat and produce the secondary connect event. The survival isolation in different historic refugia may be one of the factors responsible for two clades (Song et al., 2017). Similar conclusions have been reported in other studies. For example, the allopatric speciation for Lateolabrax maculatus and L. japonicus was speculated to be the long-time isolation during the Pleistocene ice age (Liu et al., 2006). The significant genetic differentiation for Pennahia argentata (Han et al., 2012), Penaeus japonicus (Tzeng et al., 2004), and Scomber japonicus (Yan et al., 2015) between Chinese and Japanese populations was also detected, and the land bridge formed due to the decline of sea level may block the gene exchange among them.
4.3 Gene communication among populations
Moreover, the results of the present study suggested that the gene communication between the Chinese and Japanese populations was still blocked after the glacial period, indicating that some current isolation mechanisms were maintaining the population genetic differences. Marine currents were known to play an important role in constructing the phylogeographic patterns of marine fishes (Huyghe and Kochzius, 2018). At the mercy of the currents, the marine organisms may be transported for long distance, which can effectively increase the gene exchange among populations (Song et al., 2013). However, external factors may not be always effective for some special cases, such as some species with benthic habits or rockfish (Huyghe and Kochzius, 2018). The high population genetic differentiation between the two groups indicated their limited gene communication. Most marine fishes prefer living in the nearshore waters due to the high primary productivity (Volk et al., 2021). Therefore, despite the long larval dispersal duration and strong swimming ability of adults, the offshore dispersal may not be adverse for their survival, and the Kuroshio Current may have limited functions on the gene flow between the Chinese and Japanese populations.
It’s worth nothing that population DB exhibited genetic heterogeneity with other Chinese populations by two markers. According to the results of mitochondrial DNA, this population was inferred to undergo the independent evolution event (Song et al., 2017). The Daya Bay is a semi-closed subtropical embayment in the South China Sea and the population dispersal may be limited by its special geographical location. The isolation events of different refugia may also be the possible reason for its genetic heterogeneity. Low genetic differentiation among other Chinese populations may be related to the existence of the common wintering ground in the Yellow Sea, and the coastal currents may also promote individual activities. In any case, population DB needs extra attention.
In this study, the phylogeographic pattern of K. punctatus was examined by AFLP and ISSR markers, and the results confirmed their effectiveness for population genetic study. These two markers can provide the reliable results and detect genetic differentiation at a low cost. Consistent with previous studies, there are two highly differentiated groups at the nuclear gene level and they should be treated as two separate genetic management units. The climate changes in Pleistocene periods and habits of nearshore life may be the main reasons for the formation and maintenance mechanism, and the coastal currents could benefit the population gene exchange.
Acknowledgements: We thank Dr.Takashi Yanagimoto for collecting samples. -
Figure 2. UPGMA tree based on genetic distance among individuals of K. punctatus by AFLP (left) and ISSR (right) markers.
Table 1. Sample information and genetic diversity information of K. punctatus
Populations ID Date of
collectionn Number
of lociNumber of
polymorphic
lociProportion of
polymorphic
loci/%Nei’s genetic
diversityShannon’s
diversity indexAFLP Aomori AM 2006-05 17 192 62 32.29 0.091 8 0.140 1 Tokyo Bay TB 2007-06 27 196 67 34.18 0.080 1 0.126 8 Nagasaki NG 2007-06 27 191 66 34.55 0.077 1 0.123 1 Qingdao QD 2006-04 17 198 70 35.35 0.079 7 0.128 6 Yellow River
EstuaryYR 2007-04 14 195 62 31.79 0.069 5 0.112 8 Zhoushan ZS 2006-05 14 196 65 33.16 0.091 8 0.141 9 Chengshantou CS 2007-05 19 196 74 37.75 0.089 1 0.142 0 Kongdong Island KI 2007-05 16 195 62 31.79 0.061 7 0.102 4 Daya Bay DB 2006-04 15 182 50 27.47 0.062 6 0.099 7 Total 166 214 99 46.26 ISSR Aomori AM 2006-05 18 168 120 71.42 0.179 1 0.277 Nagasaki NG 2007-06 17 167 126 75.44 0.191 1 0.292 3 Qingdao QD 2006-04 18 168 128 76.19 0.182 3 0.284 Zhoushan ZS 2006-05 17 163 118 72.39 0.182 7 0.280 8 Daya Bay DB 2006-04 18 165 127 76.96 0.184 5 0.287 7 Total 202 176 87.13 
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Table 2. Polymorphism information of primers based on two markers
AFLP primers E-ACC/
M-CTAE-AGA/
M-CTGE-AGG/
M-CTTE-ACG/
M-CTCE-AGA/
M-CTATotal Number of loci 42 71 39 23 39 214 Polymorphic loci 19 31 17 12 20 99 Proportion of polymorphic loci/% 45.23 43.66 43.59 52.17 51.28 46.26 ISSR primers UBC834 ISSR4 UBC841 ISSR62 Total Number of loci 41 58 62 41 \ 202 Noumber of polymorphic loci 39 54 50 33 \ 176 Proportion of polymorphic loci/% 95.12 93.10 80.65 80.49 \ 87.13
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Table 3. Pairwise Fst values (below) and genetic distance (above) among K. punctatus populations by AFLP marker
AM TB NG QD ZS YR KI CS DB AM 0.006 8 0.006 2 0.062 6 0.060 9 0.058 9 0.060 7 0.063 4 0.067 8 TB 0.034 49* 0.001 4 0.067 8 0.069 4 0.068 8 0.068 9 0.070 9 0.074 7 NG 0.032 46* −0.006 05 0.067 1 0.068 9 0.066 3 0.064 9 0.067 2 0.071 1 QD 0.337 90** 0.352 47** 0.361 87** 0.002 9 0.003 6 0.002 8 0.002 6 0.005 5 ZS 0.338 42** 0.362 06** 0.373 36** −0.008 73 0.003 2 0.004 8 0.005 5 0.008 YR 0.333 09** 0.362 11** 0.366 64** −0.001 66 −0.007 83 0.004 1 0.003 0.008 3 KI 0.351 59** 0.371 7** 0.370 26** −0.004 98 0.012 15 0.006 18 0.004 4 0.007 CS 0.351 59** 0.375 12** 0.374 37** −0.005 03 0.019 2* −0.003 76 0.013 26 0.004 8 DB 0.351 59** 0.413 14** 0.415 39** 0.0257 2*0.052 09* 0.0567 4*0.047 01* 0.022 2 Note: * Significant values after Bonferroni correct at 5% (P<0.05); ** significant values after Bonferroni correct at 1% (P<0.01).
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Table 4. Pairwise Fst value (below) and genetic distance (above) between K. punctatus populations by ISSR marker
AM NG QD ZS DB AM 0.005 6 0.105 6 0.109 6 0.127 9 NG −0.006 8 0.109 0.116 0.129 6 QD 0.284 66* 0.273 59* 0.004 7 0.014 9 ZS 0.291 24* 0.284 81* −0.01 0.015 7 DB 0.323 67* 0.307 88* 0.032 21* 0.034 13* Note: * Significant values after Bonferroni correct at 5% (P<0.05); ** significant values after Bonferroni correct at 1% (P<0.01).
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Table 5. AMOVA of K. punctatus by AFLP and ISSR markers
Source of variation AFLP ISSR Df Sum of squares Variance components Percentage of variation/% Df Sum of squares Variance components Percentage of variation/% All populations Among groups 1 492.437 5.898 97 36.42 1 368.878 8.188 28 36.42 Among populations within groups 7 88.605 0.139 72 0.86 3 71.015 0.212 04 0.86 Within populations 157 1 594.434 10.155 63 62.71 83 1 654.971 19.939 40 62.71 Total 165 2 175.476 16.194 02 87 2 094.864 28.339 73 Japanese group Among populations 2 29.287 0.175 5 1.63 1 18.024 −0.138 11 −0.68 Within populations 68 718.911 10.572 22 98.37 33 674.490 20.439 10 100.68 Total 70 748.197 10.747 71 34 692.514 20.300 98 Chinese group Among populations 5 59.319 0.128 31 1.29 2 52.991 0.389 91 1.95 Within populations 89 875.523 9.837 34 98.71 50 980.480 19.609 61 Total 94 934.842 9.965 65 52 1 033.472 19.999 52
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