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. 2011 Nov 4;109(2):321–330. doi: 10.1093/aob/mcr280

Genetic consequences of anagenetic speciation in Acer okamotoanum (Sapindaceae) on Ullung Island, Korea

Koji Takayama 1,*, Byung-Yun Sun 2, Tod F Stuessy 1
PMCID: PMC3268531  PMID: 22056411

Abstract

Background and Aim

Anagenesis (also known as phyletic speciation) is an important process of speciation in endemic species of oceanic islands. We investigated genetic variation in Acer okamotoanum, an anagenetically derived species endemic to Ullung Island, South Korea, to infer genetic consequences of anagenesis in comparison with other groups that have undergone cladogenesis (and adaptive radiation).

Methods

We examined genetic variation based on eight polymorphic microsatellite markers from 145 individuals of A. okamotoanum and 134 individuals of its putative progenitor A. mono. We employed standard population genetic analyses, clustering analyses, Bayesian clustering analyses in STRUCTURE and bottleneck analyses.

Key Results

Based on both the Neighbor–Joining tree and Bayesian clustering analyses, clear genetic distinctions were found between the two species. Genetic diversity in terms of allelic richness and heterozygosity shows slightly lower levels in A. okamotoanum in comparison with A. mono. Bayesian clustering analyses showed a relatively high F-value in the cluster of A. okamotoanum, suggesting a strong episode of genetic drift during colonization and speciation. There was no clear evidence of a bottleneck based on allelic frequency distribution and excess of observed heterozygotes, but the M-ratio indicated a historical bottleneck in several populations of A. okamotoanum. No geographical genetic structure within the island was found, and the genetic variation among populations of A. okamotoanum was quite low.

Conclusions

We hypothesized that genetic consequences of oceanic-endemic plants derived via anagenesis would be quite different from those derived via cladogenesis. Populations of A. okamotoanum form a cluster and are clearly differentiated from A. mono, which suggests a single origin for the anagenetically derived island endemic. No pattern of geographical differentiation of populations occurs in A. okamotoanum, which supports the concept of initial founder populations diverging through time by accumulation of mutations in a relatively uniform environment without further specific differentiation.

Keywords: Acer okamotoanum, Acer mono, adaptive radiation, anagenesis, cladogenesis, microsatellites, oceanic island, phyletic speciation, population genetics, Ullung Island

INTRODUCTION

Oceanic islands are valuable as natural laboratories of plant evolution, and they have long fascinated evolutionary biologists (Darwin, 1842; Wallace, 1881; MacArthur and Wilson, 1967; Carlquist, 1974; Grant, 1996; Whittaker, 1998). High levels of endemism, isolation from continental source areas and known geographical ages combine to make oceanic islands appealing for studying evolution, especially in contrast to more complex continental ecosystems.

One of the well-known evolutionary phenomena in oceanic islands is speciation via cladogenesis (involved with adaptive radiation), such as seen in the silverswords (Baldwin, 1997; Baldwin and Wessa, 2000) and lobelioids (Givnish et al., 2004, 2009; Friar et al., 2008) in the Hawaiian Islands, Aeonium (Mes et al., 1996; Jorgensen, 2002; Mort et al., 2002) and Echium (Böhle et al., 1996; Marrero-Gómez et al., 2000) in the Canary Islands, Scalesia (Itow, 1995; Bisconti et al., 2001; Nielsen et al., 2003; Nielsen, 2004) in the Galapagos Islands, Robinsonia (Crawford et al., 1992, 1993) in the Juan Fernández Islands and Crepidiastrum (Ito and Ono, 1990; Sang et al., 1995; Ito and Pak, 1996), Pittosporum (Ito et al., 1997) and Symplocos (Soejima et al., 1994) in the Bonin Islands. During cladogenesis in adaptively radiating groups, several lines of speciation occur from a single lineage by selection within markedly different ecological zones that result in different morphological or physiological traits (Carlquist, 1974; Futuyma, 1997; Schluter, 2001; Rundell and Price, 2009).

A contrasting speciation process is presented by ‘anagenesis’ (also known as phyletic speciation), in which initial founder populations simply diverge through time without further specific differentiation (Stuessy et al., 1990, 2006; Stuessy, 2006; Whittaker et al., 2008). Stuessy et al. (2006) considered that anagenesis was much more important in oceanic islands than previously believed, accounting for levels of endemic specific diversity from 7 to 88 %, with a mean for all islands of 25 %. In particular, low-elevation islands with low habitat heterogeneity yield higher levels of anagenesis (Stuessy et al., 2006), such as seen in Ullung Island (88 %), Bonin Islands (53 %) and St Helena (53 %). Earlier molecular studies in Ullung Island using AFLP (amplified fragment length polymorphism) markers revealed high levels of genetic variation in the endemic species, Dystaenia takesimana (Pfosser et al., 2005), which is considered to have been derived anagenetically. Furthermore, there is no geographical partitioning of this variation behaving as one large population. It has been suggested (Stuessy et al., 2006) that anagenetically derived species may accumulate genetic variation through time with lack of eco-geographical partitioning of genetic variation, hence presenting a very different pattern from species that have resulted from cladogenesis. Nevertheless, only a few studies have so far critically examined the genetic consequences of anagenesis in contrast to the classical cladogenetic model. Empirical approaches from population genetic data involving comparisons of the two different modes of speciation are required to gain insights into the evolutionary processes of island endemics.

Ullung Island has certain advantages for examining the genetic consequences of anagenesis because of the simple island system and inferred high levels of anagenesis. The island is situated 137 km east of mainland South Korea and approx. 300 km west of the Japanese archipelago; the total area is 73 km2 and the highest peak is 984 m. It is of volcanic origin, with no known connections to the mainland, and with an age of 1·8 million years (Kim, 1985). The flora contains 700 vascular plant species of which 37 angiosperms are endemic (Lee and Yang, 1981). Most of these endemics are single representatives of different genera and families that might have arrived and diverged via simple anagenetic change from the nearest source area, such as South Korea and Japan. The only three exceptions are Viola (Violaceae; V. insularis and V. takesimana), Carex (Cyperaceae; C. blepharicarpa var. insularis and C. takesimensis) and Acer (Sapindaceae; A. okamotoanum and A. takesimense). In the latter case, however, morphological studies have suggested that the endemic species of Acer are not closely related to each other but rather have different progenitors (Sun and Stuessy, 1998), and molecular phylogenetic studies have strongly supported this interpretation (Ackerly and Donoghue, 1998; Pfosser et al., 2002). This paper focuses on the genetic consequences of origin of the anagenetically derived A. okamotoanum.

Acer okamotoanum is an endemic tree widely distributed in Ullung Island (Yim et al., 1981). This species is morphologically similar to A. mono Maxim. (Sun and Stuessy, 1998), which is a deciduous tree widely distributed in the cool-temperate forests of northeastern Asia, China, Manchuria, southeastern Siberia, Korea, Sakhalin and also Japan (Gelderen et al., 1994; Sun and Stuessy, 1998). Phylogenetic studies have suggested that Korean accessions of A. mono are the closest relatives of A. okamotoanum (Pfosser et al., 2002), hence suggesting that the latter might have evolved anagenetically from continental populations of the former. In his recent reviews, Crawford (2010) regarded these two species as a good example of progenitor–derivative speciation, which is one important mode of divergence, but so far much less investigated. In many cases of progenitor–derivative speciation, populations of the two species may not be resolved phylogenetically. In A. mono and A. okamotoanum, no difference in trnL-F intergenic spacer sequences was found between the two species (Pfosser et al., 2002). It is essential, therefore, that population genetic analyses with highly polymorphic molecular markers be used to investigate and compare the genetic consequences during speciation of the island endemic.

In this study we examined patterns of genetic variation in A. okamotoanum, using nuclear microsatellite markers, in comparison with A. mono. Microsatellite markers have been successfully employed in several recent studies of the genus to reveal genetic variation at the population level (Lara-Gomez et al., 2005; Gleiser et al., 2008; Kikuchi et al., 2009). Three questions were addressed. (1) What is the degree of genetic differentiation between the two species? (2) How much genetic diversity exists within A. okamotoanum in comparison with A. mono? (3) Is there genetic differentiation among populations of A. okamotoanum within the island, and, if so, how much can be observed?

MATERIALS AND METHODS

Plant material

We collected leaf samples from 145 individuals of Acer okamotoanum Nakai from seven populations in Ullung Island, and 134 individuals of A. mono Maxim. from seven populations, four from mainland South Korea and three from Japan (Table 1, Fig. 1). Leaf samples collected in the field were desiccated with silica gel in zip-lock plastic bags until use. Voucher specimens of these samples were deposited in JNU, MAK and WU (Table 1).

Table 1.

Populations of Acer okamotoanum and Acer mono from Ullung Island, South Korea and Japan analysed for genetic diversity with nuclear microsatellites

Taxon Population Locality Voucher n RA HE FIS TA
A. okamotoanum 1 South Korea: Ullung Island, Namseo-ri TS 17591 20 6·23 0·73 0·10 53
2 South Korea: Ullung Island, Namyang-ri TS 17602 21 6·58 0·73 0·21 57
3 South Korea: Ullung Island, Chusan to Nari Sun 4118 21 6·95 0·71 0·24 61
4 South Korea: Ullung Island, Seongin Mt. Sun 4144 20 6·14 0·68 0·26 52
5 South Korea: Ullung Island, Sadong-ri TS 17623 22 6·11 0·66 0·03 54
6 South Korea: Ullung Island, Naesujeon TS 17537 24 7·41 0·76 0·23 67
7 South Korea: Ullung Island, Do-dong National Forest Sun 4005 17 6·80 0·75 0·18 56
Total 145
Mean 6·60* 0·72* 0·18 57
A. mono 8 South Korea: Prov. Kyungbuk, Il-weol Mt. TS 17628 21 7·43 0·76 0·20 66
9 South Korea: Prov. Chungbuk, Sobaek Mt. TS 16047 18 8·30 0·80 0·31 70
10 South Korea: Prov. Kangwon, Odae Mt. TS 16073 21 9·60 0·84 0·32 86
11 South Korea: Prov. Kyungbuk, Palgong Mt. TS 17636 22 7·87 0·82 0·30 71
12 Japan: Prov. Shiga, Nabejiri Mt. TS 17351 22 9·65 0·85 0·27 86
13 Japan: Prov. Niigata, Shiraike TS 17301 15 7·38 0·70 0·26 59
14 Japan: Prov. Gunma, Akaguna Mt. TS 17243 15 8·38 0·82 0·25 67
Total 134
Mean 8·37* 0·80* 0·27 72
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Voucher: TS, Tod F. Stuessy; SUN, Byung-Yun Sun.

n, the total number of analysed samples; RA, allelic richness; HE, the expected proportion of heterozygotes (*, significantly different between species with P < 0·05); FIS, the inbreeding coefficient; TA, total number of alleles in eight microsatellite loci.

Fig. 1.

Fig. 1.

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Location of Ullung Island and populations of Acer okamotoanum (1–7) and Acer mono (8–14) analysed in this study.

DNA extraction and microsatellite genotyping

Total genomic DNA was extracted from dried leaves using the DNeasy 96 Plant Kit (Qiagen, Hilden, Germany) or the CTAB (cetyltrimethyl ammonuim bromide) protocol (Doyle and Doyle, 1987). We tested 24 published markers for amplification of microsatellite loci (Pandey et al., 2004; Terui et al., 2006; Kikuchi and Shibata, 2008). PCR amplification was performed using the standard protocol of the Qiagen Multiplex PCR Kit (Qiagen, Hilden, Germany) with single-plex PCR (one primer pair in a reaction) in a final volume of 2 µL under the following conditions: initial denaturation at 95 °C for 15 min, followed by 35 cycles of denaturation at 95 °C for 30 s, annealing at 57 °C for 90 s and extension at 72 °C for 60 s. The last cycle, a final extension step, was performed at 60 °C for 30 min. The PCR products were run on an agarose gel, and 11 markers (MAP9, 40, Aca14, 24, Am116, 118, 258, 607, 668, 742 and 775) were consistently amplified in the two species. Next, the 11 loci were amplified using fluorescent dye-labelled primers (6-FAM, VIC or NED) in the multiplex PCR (11 primer pairs in a reaction) under the same conditions as in the amplification test, and run on an automated sequencer (ABI 3130xl, Applied Biosystems, Warrington, UK). Scoring of fluorescence peaks was performed using GeneMapper 4·0 (Applied Biosystems), of which eight markers were polymorphic, two (Aca14 and MAP40) were monomorphic and the final one (Am258) was an ambiguous peak pattern in several samples; we selected the eight polymorphic markers for future analyses.

Data analysis

The statistical significance of deviation from Hardy–Weinberg equilibrium (HWE), and linkage disequilibrium between loci in each population, were tested with the Markov chain method (10 000 dememorisation steps, 1000 batches, 500 iterations per batch) using GENEPOP 4.0 (Raymond and Rousset, 1995). In cases where observed genotypic frequencies deviated significantly from HWE expectations, null allele frequency was calculated following Brookfield (1996) using Micro-Checker 2.2.3 (Van Oosterhout et al., 2004).

Genetic diversity was evaluated for each species and population, in terms of allelic richness (RA), the expected proportion of heterozygotes (HE), the inbreeding coefficient (FIS) and the total number of alleles using FSTAT 2.9.3.2 (Goudet, 1995). Allelic richness was standardized for 15 individuals based on the minimum sample size of populations using the rarefaction method (Hurlbert, 1971).

Genetic structure among and within populations was evaluated hierarchically by analysis of molecular variance (AMOVA) using ARLEQUIN 3.5.1.2 (Excoffier et al., 2005). Significance of the variance components was tested by calculating their probabilities based on 1000 permutations. Genetic relationships among populations were evaluated by generating a Neighbor–Joining tree based on DA genetic distance (Nei et al., 1983) using Populations 1.2.30 (Langella, 1999). The significance of the best topology was estimated with 1000 bootstrap replicates. Genetic structure was also evaluated by the Bayesian clustering method (Pritchard et al., 2000; Falush et al., 2003) implemented in STRUCTURE 2.3.3 (Pritchard et al., 2000; Falush et al., 2007; Hubisz et al., 2009). Based on an admixture model with correlated allele frequency (hereafter called the F-model; Falush et al., 2003), individuals are assigned into K clusters (populations). The log-probability of the data, given a certain value of K, was calculated and compared across a range of K values to determine which one provided the best fit to the data. In the F-model, K clusters are considered to have diverged from a common ancestral population at the same time, and the clusters may have experienced different degrees of genetic drift since the divergence event (Falush et al., 2003). Therefore, the F-model can also be used to estimate the amount of genetic drift in each of the different populations, described as F-values. In this study, STRUCTURE analyses were performed in the three categories: (1) all samples from the two species; (2) A. okamotoanum; and (3) A. mono. Markov Chain Monte Carlo searches consisted of 20 000 ‘burn-in’ steps, followed by 10 000 iterations. Twenty replicate runs were performed at each K from 1 to 10 under the F-model. We adopted the hierarchical approach for the STRUCTURE analysis employing ΔK to determine the uppermost level of structure (Evanno et al., 2005).

Evidence for a recent population bottleneck was assessed by three different approaches. First we perform a graphical test to detect mode shifts from standard L-shape in allele frequency distribution (Luikart et al., 1998) using BOTTLENECK 1.2.02 (Piry et al., 1999). Secondly, we test for the presence of an excess of observed heterozygotes by using the Wilcoxon signed rank test to evaluate departure from 1:1 deficiency/excess (Cornuet and Luikart, 1996; Luikart et al., 1998) using the same program. In the test, heterozygosity excess was tested under the three mutation models, the infinite allele model (IAM; Kimura and Crow, 1964), step-wise mutation model (SMM; Ota and Kimura, 1973) and two-phase model (TPM; Di Rienzo et al., 1994) with 1000 simulation iterations. For TPM, we set 90 % single-step, 10 % multiple-step mutations with a variance among multiple steps of 12. Thirdly, M-ratios (Garza and Williamson, 2001) were used to test for bottlenecks. If the population size remains low after a bottleneck, the signature will be detectable for longer using the M-ratio than using BOTTLENECK (Garza and Williamson, 2001). We calculated the M-ratio of each population and species using M_P_VAL (available from http://swfsc.noaa.gov/textblock.aspx?Division=FED&id=3298). To test the significance of a ratio, observed values (M) were compared with a distribution of values (Mc) obtained from a simulated population given our sample size and TPM with 90 % single-step mutations and an average size of non-single-step mutation, Δg = 3·5 (Garza and Williamson, 2001). Thus, Mc represents a significant cut-off below which a bottleneck is inferred. Mc values are sensitive to using model parameters, and therefore we used two likely θ values of 2 and 20, encompassing a wide range of biologically plausible values. To estimate plausible values for pre-bottleneck effective population size (Ne), we used long-term estimators of Ne that were based on expected heterozygosity (HE) and mutation rate (μ) using equation 3·15 {Ne = HE/[4μ(1 – HE)]; Hartl and Clark, 1989} and equation 7 {Ne = [1/(1 – HE)2 – 1]/8μ; Ota and Kimura, 1973}, respectively. The first equation assumes the IAM and the second SMM. We used a common microsatellite mutation rate, 5·0 × 10−4 mutations/generations/locus (Weber and Wong, 1993).

RESULTS

Genetic data analysis

Eight microsatellite loci were used to genotype 279 individuals from 14 populations in A. okamotoanum and A. mono (Table 1). All eight loci were amplified in all samples; there were no obvious patterns that indicated the occurrence of null alleles in genotyping. An exact test for HWE across populations and loci revealed that 18 of 168 deviated from HWE (P < 0·05) after Bonferroni correction. Not in equilibrium was a single locus each in populations 1, 3, 7, 9 and 14, two loci in populations 6, 9, 10, 11 and 13, and three loci in population 12. All of these deviant cases are related to the positive FIS, indicating HWE deviation in the direction of heterozygote deficit. We detected the presence of null alleles in all of the cases using Micro-Checker (the highest frequency of a null allele was 0·365 in MAP9 of population 13; and average frequency was 0·084). There are, however, no particular loci that deviated from HWE in most populations nor particular populations in which most loci deviated from HWE; hence, we used all loci and populations for subsequent analysis. Significant linkage disequilibrium was only found in population 12 (P < 0·05) after Bonferroni correction. In this population, 11 of 28 pairs of loci were significantly deviant from linkage equilibrium. Removal of this population from analyses did not qualitatively change the results, and therefore it was retained for all analyses.

Genetic diversity

Genetic diversity parameters, allelic richness, expected proportion of heterozygotes, inbreeding coefficients and total number of alleles for the eight microsatellite loci are shown in Table 1. In comparing the two species, allelic richness and the expected proportion of heterozygotes were significantly lower in A. okamotoanum. FIS values were slightly positive for all populations within the two species (Table 1).

Genetic structure

Population structure of the two species was tested by AMOVA at different hierarchical levels with four data sets (Table 2). In all of the four data sets, most of the variation was found among individuals within populations (86·0–96·0 %). In the data sets of A. okamotoanum (Table 2B), there was little genetic variation among populations (4·0 %), which was less than half the value in the data sets of A. mono (10·5 %). AMOVA also revealed that the genetic variation of A. mono between South Korea and Japan was statistically significant at the 5 % level (Table 2C-2).

Table 2.

Summary of analyses of molecular variance (AMOVA), showing degrees of freedom (d.f.), sum of squares (SS), variance components and the total variance contributed by each component (%) and its associated significance (n = 1000 permutations)

Taxon Source of variation d.f. SS Variance components Total variance (%) P-value
(A) Acer okamotoanum and A. mono Among taxa 1 82·3 0·250 7·1 <0·001
Among populations 12 152·0 0·243 6·9 <0·001
Within populations 544 1641·3 3·017 86·0 <0·001
(B) A. okamotoanum Among populations 6 47·0 0·120 4·0 <0·001
Within populations 283 810·3 2·863 96·0
(C-1) A. mono (no regional category) Among populations 6 105·0 0·375 10·5 <0·001
Within populations 261 830·9 3·194 89·5
(C-2) A. mono (South Korea and Japan) Among regions 1 35·2 0·167 4·6 0·038
Among populations 5 69·8 0·282 7·8 <0·001
Within populations 261 830·9 3·184 87·6 <0·001
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A Neighbor–Joining tree based on DA genetic distance for each population of the two species is shown in Fig. 2. Two large clusters with 77 % bootstrap values are consistent in uniting populations of each species. In the cluster of A. mono, populations in South Korea and Japan separate into different sub-clusters. Genetic distances among populations of the same species were greater in A. mono (mean DA was 0·423) than in A. okamotoanum (mean DA was 0·165). The mean genetic distance between populations of the two species was 0·447.

Fig. 2.

Fig. 2.

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Neighbor–Joining tree of the 14 populations of Acer okamotoanum and Acer mono based on DA distance (Nei et al., 1983). For localities of populations, see Table 1 and Fig. 1. Bootstrap probabilities >50 % are shown above the branches.

With Bayesian clustering analysis by STRUCTURE using all data sets from the two species, the uppermost level of structure was at K = 2 based on the ΔK value (Fig. 3A). The ΔK value dropped sharply with K = 3, then dropped to near zero after K = 4. At K = 2, the two clusters correlated well with the two different species (Fig. 4A): cluster I comprised 97 % of the genotypes in A. okamotoanum, and cluster II comprised 95 % of the genotypes in A. mono. The F-value of cluster I (F = 0·138) was somewhat higher than that of cluster II (F = 0·005), consistent with a strong episode of genetic drift for A. okamotoanum. Next we performed analysis of populations from each species. The ΔK value in populations of A. mono clearly indicated that the uppermost level of structure was at K = 2 (Fig. 3C). On the other hand, the ΔK-value in the populations of A. okamotoanum was highest at K = 3, but the peak pattern of ΔK-values and also the log probability of the data Pr(X|K) showed no clear peak (Fig. 3B). The results of the clustering in each species at K = 2 to K = 5 are shown in Fig. 4B and C. At K = 2 of A. mono, populations from South Korea and Japan mainly form different clusters (Fig. 4C). Population 8 forms a cluster contrasting with other Korean populations at K = 3, and the Japanese cluster splits into different sub-clusters at K = 4 and K = 5. In the case of A. okamotoanum, multiple clusters occur within a population and individual, and no obvious population sub-division was found at K = 2–5.

Fig. 3.

Fig. 3.

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Results of Bayesian clustering (STRUCTURE; Pritchard et al., 2000) of Acer okamotoanum and Acer mono. (A) All the samples of A. okamotoanum, and A. mono, (B) A. okamotoanum and (C) A. mono. The upper graphs give the mean ln Pr(X|K) ± s.d. over 20 runs for each value of K. The lower graphs give ΔK of Evanno et al. (2005), showing a peak at the uppermost level of structure at the true value of K.

Fig. 4.

Fig. 4.

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Results of Bayesian clustering (STRUCTURE; Pritchard et al., 2000) of Acer okamotoanum and Acer mono. (A) All the samples of A. okamotoanum and A. mono, (B) A. okamotoanum and (C) A. mono. Vertical columns represent individual plants, and the heights of bars of each colour are proportionate to the posterior means of estimated admixture proportions. For localities of populations, see Table 1 and Fig. 1.

Population bottleneck

The mode-shift test did not detect any evidence of a bottleneck (Table 3). Excess heterozygosity was detected in population 11 of A. mono in South Korea (P < 0·05) after Bonferroni correction only under IAM (Table 3). Estimation of Ne ranged from 984·2 to 4058·8 in the populations of A. okamotoanum and from 1178·6 to 10189·5 in those of A. mono. Thus, our biologically plausible pre-bottleneck Ne values were set to 1000 (θ = 2) to 10 000 (θ = 20) for the critical values (Mc) in the two species. The M-ratio values of three populations from A. okamotoanum and one population of A. mono were consistently below Mc thresholds that assumed two different θ values. The other three populations of A. okamotoanum and four populations of A. mono had significantly lower M-ratio values at θ = 2, but not at θ = 20.

Table 3.

Summary of the parameters and results for BOTTLENECK and M-ratio analyses

Taxon Population BOTTLENECK
 M Mc
Mode shift IAM TPM SMM θ = 2 θ = 20
Acer okamotoanum 1 NS 0·027 0·844 0·980 0·560 0·723 0·605
2 NS 0·125 0·727 0·902 0·691 0·722 0·609
3 NS 0·527 0·973 0·994 0·707 0·722 0·609
4 NS 0·098 0·844 0·973 0·559 0·723 0·605
5 NS 0·422 0·986 0·986 0·560 0·727 0·616
6 NS 0·037* 0·963 0·986 0·707 0·727 0·621
7 NS 0·156 0·809 0·875 0·733 0·719 0·585
Acer mono 8 NS 0·273 0·994 1·000 0·626 0·722 0·609
9 NS 0·027 0·770 0·980 0·626 0·715 0·590
10 NS 0·006 0·844 0·973 0·735 0·722 0·609
11 NS 0·002* 0·963 0·996 0·688 0·727 0·616
12 NS 0·004 0·320 0·680 0·780 0·727 0·616
13 NS 0·273 0·527 0·629 0·653 0·711 0·571
14 NS 0·004 0·098 0·320 0·601 0·711 0·571
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NS, non-significant (P < 0·05); *significant (P < 0·05); bold, Mc-value higher than the M-value.

DISCUSSION

Genetic differentiation between the two species

Our first aim was to examine relationships and genetic differences between A. okamotoanum of Ullung Island and A. mono from continental areas (i.e. South Korea and Japan). The two species can be most easily distinguished by larger leaves, flowers and fruits in A. okamotoanum (Sun and Stuessy, 1998). In previous molecular analyses using AFLP (Sun and Stuessy, 1998; Pfosser et al., 2002), however, populations of A. okamotoanum appeared genetically mixed with mainland populations of A. mono, indicating possible multiple introductions and/or frequent genetic exchange between the two species. In contrast, in our Neighbor–Joining tree using eight polymorphic microsatellite markers, the two species form separate clusters (Fig. 2). The uppermost level of structure in STRUCTURE analysis was obviously at K = 2 (Fig. 3), and the two clusters almost correspond to the two species (Fig. 4). We included the same population (population 9) that had been nested within populations of A. okamotoanum in the AFLP analyses (‘A. mono 9’ in fig. 4 of Pfosser et al. 2002), but this population forms a clade with the other populations of A. mono in the Neighbor–Joining tree based on microsatellite markers. The discrepancy between AFLP and microsatellite analyses may be due to a difference in sampling strategy. Pooled DNA of five individuals per population was used to construct a Neighbor–Joining tree in the AFLP analyses, but in the present study a minimum of 15 individuals per population was used in the microsatellite analyses. Using a larger number of individuals in each population could provide a more accurate estimation of populational relationships. Further, the bootstrap values that supported clades of each species in the AFLP study was not high (54 % and <50 %). Based on our new microsatellite data, we conclude that clear genetic distinction exists between the two species and that A. okamotoanum has had a single origin from A. mono.

Genetic diversity within species and populations

Genetic diversity in terms of allelic richness and heterozygosity shows a slightly lower level of genetic diversity in A. okamotoanum in comparison with A. mono (Table 1). AFLP analysis (Pfosser et al., 2002) also revealed that A. okamotoanum maintained a substantially smaller number of alleles than A. mono. Frankham (1997) compiled data from oceanic island populations of various organisms and their counterparts in mainland areas, and he documented in the majority of cases a reduction in genetic diversity in the island populations. Loss of genetic diversity in island endemic plants has also been observed in the Hawaiian Islands, the Canary Islands, and the Juan Fernandez Islands (DeJoode and Wendel, 1992; Crawford et al., 2001, 2006). A bottleneck effect during immigration, relatively recent origin, small population sizes of the island endemic, loss of populations through island subsidence erosion and high levels of inbreeding in island populations might be responsible for the low levels of genetic diversity observed.

The data show that A. okamotoanum has lower genetic diversity than the progenitor A. mono. The F-value in STRUCTURE inferred for A. mono populations was close to zero (F = 0·005), whereas a high F-value was estimated for A. okamotoanum populations (F = 0·138), consistent with a strong episode of genetic drift during colonization and speciation. In our bottleneck analyses, there is a lack of mode shifts in allele frequencies or strong evidence for excess heterozygosity in A. okamotoanum, yet significant M-ratios in several of its populations suggest that these have experienced not very recent, but rather more historical (i.e. hundreds if not thousands of generations), bottleneck effects (Luikart et al., 1998; Garza and Williamson, 2001; Spear et al., 2006). Both strong genetic drift in an early stage of colonization and a historical bottleneck might have yielded the low level of genetic diversity in A. okamotoanum.

A somewhat different genetic pattern has been reported for Dystaenia takesimana (Apiaceae), another endemic in Ullung Island. Amplified fragment length polymorphism analyses revealed that the endemic species had a higher level of genetic diversity in comparison with the continental progenitor D. ibukiensis, restricted to Japan (Pfosser et al., 2005). Estimated sizes of the populations of D. takesimana analysed were similar to or larger than those of D. ibukiensis. Pfosser et al. (2005) concluded, therefore, that D. takesimana may have regained genetic diversity during or after speciation along with increasing sizes of populations. In the case of A. okamotoanum, estimated sizes of the populations analysed varied from small to larger (between 50 and 1000 plants), but clearly A. mono has a larger total population size because it is a very common species widely distributed throughout East Asia. The different sizes of populations might be one reason for differences in genetic diversity observed between these two endemic species in Ullung Island. Dystaenia takesimana and A. okamotoanum also differ in their generation times: the former is a perennial herb and the latter a tree. This may also help explain differences in patterns of genetic variation, because the nucleotide mutation rate is generally higher in more rapidly reproducing organisms (Li and Wu, 1987).

Geographical genetic structure within A. okamotoanum

Populations of A. okamotoanum show no geographical genetic structure (Fig. 4), and genetic variation among populations is quite low (Table 2). This suggests that genetic exchange among populations may occur frequently on Ullung Island. The largest distance between two populations (1–7) was approx. 7·5 km, which is nearly the furthest possible distance on the island. Acer okamotoanum is a common species in the forest, and all individuals appear to form a panmictic unit. As is the case with other Acer species, A. okamotoanum has a fruit (samara) with flattened wings that promotes long-distance dispersal of seeds and subsequent genetic exchange. On the other hand, A. mono shows a more fragmentary genetic structure in STRUCTURE analysis (Fig. 4), and genetic distances between populations are greater. The higher level of genetic differentiation within A. mono may coincide with broader morphological variation, especially in leaves, and, therefore, an extensive number of intraspecific taxa have been recognized (Gelderen et al., 1994). Acer okamotoanum shows a very limited range of morphological variation in comparison with A. mono; this may be explained by a loss of variation due to founder effect and/or non-environmental canalization (i.e. uniform environment) within Ullung Island.

As A. okamotoanum is clearly anagenetically derived from A. mono, it is useful to examine the genetic consequences of this process. In cladogenesis, several lines of speciation occur from a single lineage by rapid selection within markedly different ecological zones that result in different morphological or physiological traits (Carlquist, 1974; Futuyma, 1997; Schluter, 2001; Rundell and Price, 2009). During this process, the morphologically or physiologically diverging populations accumulate some genetic differences, but more conspicuous is a partitioning of the gene pool into restricted genetic lines (Schluter, 1996). In anagenesis, it has been hypothesized that initial founder populations simply diverge through time without further internal specific differentiation (Stuessy et al., 1990, 2006; Stuessy, 2006; Whittaker et al., 2008). Low elevation islands with low habitat heterogeneity might be expected to yield higher levels of anagenesis (Stuessy et al., 2006), such as in Ullung Island. As a consequence, anagenetically derived species would be expected to accumulate genetic variation through time without eco-geographical partitioning of genetic variation. Acer okamotoanum and D. takesimana, mentioned earlier (Pfosser et al. 2005), both show panmictic populations. Higher levels of total genetic variation exist in D. takesimana than in A. mono, however, which may suggest the former having passed through more generations or a less severe initial bottleneck than the latter.

ACKNOWLEDGEMENTS

This work was supported by a Japan Society for the Promotion of Science (JSPS) Postdoctoral Fellowship for Research Abroad (grant no. 526 to K.T.), the Austrian National Science Foundation (FWF) (grant no. P21723-B16 to T.S.) for support of laboratory work, and the Korea Environmental Industry & Technology Institute (KEITI), Eco-star project (grant no. 052-08-071 to B.-Y.S.) for support of field collections.

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