<I>Heliopora Coerulea</I>

Home , Acropora digitifera, Blue coral, Heliopora, Marker 32, Seriatopora hystrix

Bull Mar Sci. 90(1):233–255. 2014 research paper http://dx.doi.org/10.5343/bms.2012.1105

Genetic structure and cryptic speciation in the threatened reef-building coral Heliopora coerulea along Kuroshio Current

1 Department of Marine Biology 1 * and Environmental Sciences, Nina Yasuda 2 University of Miyazaki, Faculty Coralie Taquet of Agriculture, Gakuen- Satoshi Nagai 3 kibanadai-nishi-1-1, Miyazaki, 4 Japan 889-2192. Miguel Fortes 5 2 Tokyo Institute of Technology Tung-Yung Fan 6 2-12-1 Ookayama, Meguro-ku, Niphon Phongsuwan Tokyo, Japan 152-8552 and UMR Kazuo Nadaoka 2 241 Ecosystèmes Insulaires Océaniens, Université de la Polynésie Française, B.P. 6570, ABSTRACT.—The blue coral Heliopora coerulea (Pallas, 98702 FAA’A Aéroport, Tahiti, French Polynesia. 1766), a reef-building coral, is the sole member of the alcyonarian order Coenothecalia. Recently, local and global 3 National Research Institute anthropogenic stresses on its habitat have led to this species of Fisheries Science, Aquatic being listed as threatened. Strong genetic differentiation has Genomics Research Center, been observed between populations of H. coerulea that have 2-12-4 Fukuura, Kanazawa-ku, been found only 500 m apart. Despite the wide geographical Yokohama, Kanagawa 236-8648, distribution of H. coerulea in the Indo-Pacific oceans, such Japan. localized genetic structuring has led to speculation about 4 Marine Science Institute CS whether H. coerulea populations are completely isolated University of the Philippines over different geographic scales (including speciation). To Diliman, Quezon City 1101, The investigate gene flow in H. coerulea, we genotyped seven Philippines and Aurora Marine Research and Development microsatellite loci in 611 H. coerulea colonies collected from Institute, Baler, Aurora, The along the Kuroshio Current. Genetic admixture analysis Philippines. and principal coordinate analysis suggested the existence of two cryptic species in the studied area. In addition, distinct 5 National Museum of Marine Biology and Aquarium, 2 typical gross morphologies were shown by both these cryptic Houwan Road, Checheng, species. Patterns of isolation by distance were more obvious Pingtung, Taiwan, R.O.C. and significant when each genetic clade was analyzed separately than when the populations were analyzed together. 6 Marine and Coastal Biology and Ecology Unit, Phuket Marine In addition, we found that some populations had extremely Biological Center, 51 Sakdidej low genotypic diversity. These findings indicate that these Rd., Muang District, Phuket populations may be more threatened than previously believed 83000, Thailand. and emphasize the threatened status of H. coerulea. * Corresponding author email: .

Submitted: 19 December, 2012. Accepted: 24 June, 2013. Available Online: 18 November, 2013.

Coral reef ecosystems have some of the highest biodiversity levels among the coastal ecosystems and account for food, resources, and services worth an estimated $30 billion annually (Cesar et al. 2003). Recent anthropogenic stresses that are

Bulletin of Marine Science 233 © 2014 Rosenstiel School of Marine & Atmospheric Science of OA the University of Miami Open access content 234 Bulletin of Marine Science. Vol 90, No 1. 2014 intimately associated with the severe decline in coral reefs worldwide include land- based pollution, overexploitation, and global warming. Greater than one-third of the coral reefs are estimated to disappear or be under threat in the next three decades (Wilkinson 2008). Because the coral reef ecosystem is under various environmental threats (Hughes et al. 2003, Pandolfi et al. 2003), conservation and management plans are necessary. Marine protected areas (MPAs) or marine reserves are beneficial for conserving this type of ecosystem and can produce almost immediate and long- lasting effects (Halpern and Warner 2002). MPAs play a key role in protecting the target fraction of a key habitat and also act as a source of recruits, which are needed to maintain and replenish populations through larval dispersal. Therefore, designing effective MPAs requires knowledge of larval dispersal of key species in the ecosystems, such as reef-building corals. Despite the importance of reef-building corals, population genetic studies of these corals have generally lagged behind those of most other organisms owing to the difficulties in developing easily accessible and highly polymorphic genetic markers, e.g., the mitochondrial genome of these corals is highly conserved (Shearer et al. 2002, Ridgway and Gates 2006), and there is a low abundance of nuclear microsatellites in their relatively small genomes (Marquez et al. 2000). Previous population genetic studies of corals revealed that larval dispersal is more restricted than previously thought and that coral larvae generally disperse over an area of 70 km or less; this holds true even for spawner species with relatively long larval durations (i.e., >3 d; Van Oppen and Gates 2006). Given that low reef-connectivity leads to smaller meta- population size, an MPA unit of a finer scale than that used for species with strong reef-connectivity may be required. However, not many studies have examined the genetic structures of reef-building coral species with short larval dispersal potential (i.e., most larvae settle in <1 d). Population genetic studies of Seriatopora hystrix Dana, 1846 revealed that most larvae settle within a few hours and that larval dispersal occurs on a scale of <100 m (Underwood et al. 2007). Another study revealed that high-latitude S. hystrix populations are supplemented by infrequent long-distance migrants from the Great Barrier Reef and appear to have adequate population sizes to maintain viability and resist severe loss of genetic diversity (Noreen et al. 2009). These findings suggest that population genetic studies of species showing limited larval dispersal require different geographic scales, especially in areas where strong oceanic currents govern larval dispersal. Heliopora coerulea Pallas, 1766 is a type of octocoral but has a hard skeleton like other reef-building corals. Heliopora coerulea has been classified as a threatened species by the International Union for Conservation of Nature and Natural Resources because although specific population trends are unknown, population reduction can be inferred from declines in habitat quality based on the combined estimates of both destroyed reefs and reefs at the crit- ical stage of degradation within its distribution range (Obura et al. 2008). Heliopora coerulea is a gonochoric brooding species with low fecundity whose oocytes are fer- tilized internally and larvae are brooded externally on the female surface once a year (Babcock 1990, Liu et al. 2005). A laboratory experiment showed that most H. coerulea larvae (74%) settle within 1 d of release (Harii et al. 2002), which is indicative of a very short larval dispersal range. A previous study showed strong genetic differentiation between two populations with a high FST value (0.123) across the reef crest in the Shiraho reef along southwest Japan, even though they are separated by only 500 m (Yasuda et al. 2010). Despite the wide geographical distribution of H. coerulea in the Yasuda et al.: Cryptic speciation in blue coral 235

Indo-Pacific oceans, such localized genetic structuring has led to speculation about whether H. coerulea populations are completely isolated over different geographic scales and whether speciation occurs in the peripheral populations. In the present study, we examined the population genetic structure of H. coerulea along the Kuroshio Current at different geographic scales by using microsatellite markers. We found the following: (1) two cryptic species coexist along the Kuroshio Current, (2) strong genetic differentiation and significant IBD patterns within each of the two cryptic species, and (3) some populations of H. coerulea have extremely low genetic diversity but no clear correlation was found along different latitudes.

Materials and Methods

Study Area and Sampling.—To examine larval dispersal, we collected 611 H. coerulea specimens from 47 sampling sites along the Kuroshio Current including three intensive local samplings (Yaeyama in Japan, south Taiwan, and Verde Island Passage in the Philippines) from 2005 to 2011 (Fig. 1). We collected specimens by scuba or skin diving and obtained 1–2-cm fragments to minimize the damage to corals. All the specimens were preserved in 99.5% ethanol immediately after sampling. We collected the specimens at different depths, ranging from almost 0 (the top of the microatoll) to 14 m. The geographic distances between sampling sites ranged from approximately 1 km (e.g., SENW and SENA in Sekisei Lagoon, TWNC and TWND) to >1800 km (e.g., Amami and Philippines) (Table 1, Fig. 1). Although there are many variations of coral morphology, we classified the specimens into two typical morphs: small branch and flat shapes (Fig. 2). We collected as many H. coerulea samples as possible within 45 min. Therefore, the sampling size n( < 23) was mainly constrained by the maximum number of coral colonies that could be found within the sampling period. No colony was sampled twice, and all colonies were at least 3 m apart. DNA Extraction and PCR Amplification.—Genomic DNA was extracted using a DNeasy Blood & Tissue Kit (QIAGEN) by following the manufacturer’s proto- col. Seven nuclear microsatellite loci, Saki 06, Saki 08, Mayu41, Mayu49, Miho33, Emi20, and Kumiko02 (Yasuda et al. 2008, 2010) were selected on the basis of their stability during PCR amplification. All loci were amplified and genotyped using previously described protocols (Yasuda et al. 2008). Clonal Structure.—In the present study, genetic diversity refers to the amount of variation among individual genes in a population, whereas genotypic diversity refers to the number of multilocus genotypes present in a population that varies on the level of whole organism and is a result of asexual replication (Baums et al. 2006). We identified clones using the program GENCLONE 2.0 (Arnaud-Haond and Belkhir 2007, Arnaud-Haond et al. 2007). The observed identical multilocus genotypes can be either the result of cloning (fragmentation or asexual propagation) or two different genotypes originating from two distinct sexual reproduction events that share the same alleles for all genotyped loci. We determined the number of genets (unique genotypes) at each site (Ng) and compared this to the total number of individuals (Ng/N), which represents a maximal estimate of the contribution of asexual reproduction to localized recruitment. Genotypic diversity was estimated using the

Shannon-Wiener index: H´ = –pi ln pi, where pi is the frequency of a particular multilocus genotype (Arnaud-Haond et al. 2007). Genotypic diversity was also calculated 236 Bulletin of Marine Science. Vol 90, No 1. 2014

Figure 1. Heliopora coerulea sampling sites and distribution of the 2 genetic clades identified by Structure analysis. Black: clade A, white: clade B.

2 using the Simpson’s unbiased index: D = (1 – ∑pi )n ⁄ (n – 1), where n is the sample size (Arnaud-Haond et al. 2007). Both measurements take into account genotypic richness and the proportion of each genotype within the local population. Although these indexes have a minimum value of 0, when all isolates have the same genotype, H´ becomes >1 depending on the total sample size, while D has a maximum value of 1 when all colonies have different genotypes. For genotypic evenness, we calculated the Fager index of evenness: E = (D – Dmin)⁄(Dmax – Dmin) (Arnaud-Haond et al. 2007). E approaches 0 when a single clone dominates. All the indexes, H´, D, and E, were calculated with GENCLONE v2.1 (Arnaud-Haond and Belkhir 2007). The relationship between genotypic richness, genetic diversity, and latitude were plotted and regression coefficients were calculated using Microsoft Excel. Although we were aware that the number of individuals used for population genetic analysis was small, 23 samples with genets >6 were used for the subsequent analysis to use as many samples as possible to examine population genetic structure. We Yasuda et al.: Cryptic speciation in blue coral 237 E: Pid 0.00004 0.04558 0.05273 0.00653 0.34214 0.02954 0.00038 0.00010 0.08159 0.00105 0.00091 0.01738 0.00004 0.00583 0.00225 0.00217 0.00006 0.01978 0.00654 0.00698 0.00002 - - – E 0.95 0.28 0.72 0.35 1.00 1.00 1.00 0.59 1.00 0.98 0.54 0.97 0.98 1.00 0.97 1.00 0.96 1.00 - H' 2.84 0.31 0.00 0.50 0.49 1.10 1.61 1.61 0.41 1.39 1.75 0.00 0.38 2.49 1.75 3.37 3.60 1.10 1.33 1.10 : expected heterozygosity, heterozygosity, expected : E - D 0.96 0.14 0.00 0.40 0.23 1.00 1.00 1.00 0.29 1.00 0.95 0.00 0.25 0.97 0.95 1.00 0.99 1.00 0.90 1.00 - 0.83 0.07 0.03 0.40 0.05 0.67 1.00 1.00 0.29 1.00 0.86 0.03 0.25 0.80 0.86 1.00 0.92 1.00 0.80 1.00 Ng/n shannon-Weaver index of diversity, index of diversity, shannon-Weaver 3 1 2 2 2 5 5 2 4 6 1 2 6 1 3 4 3 20 12 29 45 Ng H': 5 3 5 5 7 4 7 8 7 1 3 5 3 n 24 41 40 41 15 31 29 49 E H 0.548 0.408 0.547 0.396 : observed heterozygosity, H heterozygosity, observed : O O H H 0.650 0.472 0.626 0.371 Longitude 124°11'19.88"E 129°21'49.69"E 128°04'53.10"E 128°04'46.00"E 128°04'35.40"E 128°04'35.80"E 127°44'05.78"E 126°50'48.50"E 126°47'55.60"E 126°50'01.70"E 126°49'40.82"E 126°51'58.40"E 125°21'27.50"E 125°25'55.70"E 125°20'16.20"E 125°10'42.90"E 124°15'19.10"E 124°18'26.60"E 124°15'02.00"E 124°12'51.20"E 124°07'18.20"E D : Simpson's unbiased index of diversity, observed in the present study. study. present the in observed Latitude 26°32'16.20"N 26°32'16.00"N 26°31'50.00"N 26°31'45.40"N 26°21'50.21"N 26°20'55.00"N 26°22'09.90"N 26°21'15.00"N 26°19'56.70"N 26°20'58.80"N 24°47'27.39"N 24°43'44.90"N 24°43'02.70"N 24°51'52.10"N 24°21'47.00"N 24°32'51.85"N 24°19'41.94"N 24°31'36.00"N 24°20'56.80"N 24°29'19.50"N 28°07'02.53"N A B B B Clade Heliopora coerulea Flat Flat Flat Flat Flat Flat Flat Major shape Intermediate Intermediate Intermediate Intermediate Intermediate Small branch Small branch Small branch Small branch Small branch Small branch Small branch Small branch Small branch 5.0 5.0 3.0 6.0–9.0 2.5–6.0 2.5–3.0 2.5–4.0 5.0–6.5 3.0–8.0 0.4–0.7 4.0–9.0 0.0–2.0 1.5–2.0 5.0–11.0 2.0–12.0 8.0–10.0 8.0–10.0 3.0–10.0 6.0–10.0 2.0–10.0 12.0–14.0 Ng/n: proportion of unique mutilocus genotype, Depth (m)* Code AKA SHIR AMM MIYAY 1 2 3 4 No. Ng: number of genets, Sankaku Iwa Ooura Bay Jinza KumeA-Ichiunda Ooura bay Chiribishi Oura Bay Okinose1 Oura Bay Okinose2 Mizugama KumeAA-Akashita KumeB-Ichimonji Shimosaki KumeE-NorthLine KumeF-DoubleKurebasu Shiraho reef Bora River Beach Yoshino Shiratorisaki Akaishi Maezato Ibaruma Miyara Kabira Amami Oshima Okinawa Mainland Kume n: number of samples, genotypic evenness, Pid: probability of identity. Collection site Table 1. Details of sampling collection location and sites and clonality in clonality and sites and location collection sampling of Details 1. Table Miyako Yaeyama 238 Bulletin of Marine Science. Vol 90, No 1. 2014 Pid 0.09283 0.00081 0.00074 0.00168 0.00372 0.00011 0.00011 0.00126 0.00002 0.00001 0.00001 0.00003 0.00001 0.00006 0.00836 0.00080 0.05273 0.00020 0.00001 <0.00001 <0.00001 <0.00001 - - E 0.99 0.88 1.00 0.93 1.00 1.00 0.99 1.00 1.00 1.00 1.00 0.98 1.00 1.00 1.00 0.94 0.84 1.00 0.95 H' 2.93 2.02 1.61 0.00 2.32 3.20 1.61 2.62 2.71 3.37 2.30 2.30 2.16 2.30 2.30 2.30 1.83 1.63 0.00 2.08 2.28 D 0.99 0.87 1.00 0.00 0.93 1.00 1.00 0.99 1.00 1.00 1.00 1.00 0.98 1.00 1.00 1.00 0.91 0.79 0.00 1.00 1.00 0.95 0.50 1.00 0.25 0.60 0.96 1.00 0.93 1.00 1.00 1.00 1.00 0.90 1.00 1.00 1.00 0.62 1.00 0.33 1.00 1.00 Ng/n 5 1 5 9 8 6 1 9 7 19 10 12 25 14 15 10 10 10 10 10 10 Ng 5 4 5 6 3 9 7 n 20 20 20 26 15 15 10 10 10 10 10 10 10 13 E H 0.426 0.440 0.364 0.653 0.660 0.530 0.564 0.614 0.613 0.584 0.572 0.549 0.642 0.364 0.502 0.592 O H 0.436 0.514 0.464 0.653 0.743 0.530 0.529 0.600 0.614 0.667 0.568 0.614 0.600 0.464 0.508 0.605 Longitude 124°01'44.90"E 124°07'51.20"E 124°08'58.60"E 124°05'31.90"E 124°09'46.10"E 123°57'14.70"E 124°00'14.30"E 123°53'21.70"E 123°56'53.50"E 120°48'20.50"E 120°44'41.77"E 120°49'19.19"E 120°44'43.40"E 120°46'08.90"E 120°42'18.96"E 120°42'55.01"E 120°50'23.12"E 120°50'28.66"E 120°52'27.35"E 120°58'19.96"E 120°57'10.50"E Latitude 24°13'24.50"N 24°15'31.20"N 24°17'15.10"N 24°16'16.30"N 24°19'13.40"N 24°21'10.30"N 24°20'43.30"N 24°24'05.50"N 24°21'40.10"N 21°56'12.10"N 21°55'53.12"N 21°56'01.07"N 21°55'54.60"N 21°57'12.20"N 21°59'43.29"N 21°58'23.27"N 13°41'07.29"N 13°41'24.17"N 13°39'45.18"N 13°37'51.46"N 13°38'38.36"N B B B B A A A A A A A A A B B B Clade Flat Flat Flat Flat Flat Flat Flat Flat Flat Flat Flat Flat Major shape Small branch Small branch Small branch Small branch Small branch Small branch Small branch Small branch Small branch 1.8–5.4 1.7–9.9 2.0–9.0 2.2–8.7 1.0–3.0 2.5–3.0 3.9–8.5 1.5–8.6 5.0–9.8 4.0–7.0 1.4–2.5 2.3–5.1 2.8–4.7 4.4–5.7 2.5–6.8 4.4–6.6 2.0–4.0 2.5–6.5 2.5–3.2 9.6–11.4 4.0–10.0 Depth (m)* Code SESE SESA SESB SENA IROM SENW TWNF TWNE TWNA TWNB TWNC TWND TWNG VCABA VMARB VMARC 5 6 7 8 9 11 10 12 13 14 15 16 17 18 19 20 No. SESA-Kengu SESB-East1 SESC-East2 SESD-Taketomi SESE-Ishigaki South SEN-Yonara East SEN-Yonara SENB-Kohama SENA-Yonara West SENA-Yonara Iriomote-north Kenting Park-Little Bay Kenting Park-Chanfan Rock Kenting Park-Leitashin Kenting Park-Outlet Kenting Park-Taioshi Kenting Park-Wanlitong Hongchaikeng VCAB-CabanA-Saddel VCAB-CabanB-Dari laot VMAR-MaricabanA- Snorkell N Betlehem VMAR-MaricabanB- Dulo VMAR-MaricabanC- Site 3 Yaeyama Table 1. Continued. Table Collection site Taiwan South Taiwan Philippins Verde Passage Verde Philippins Yasuda et al.: Cryptic speciation in blue coral 239

Pid 0.00001 0.00066 0.00129 0.02852 <0.00001 E 0.95 1.00 0.94 0.95 0.79 0.91 H' 2.28 2.08 1.83 1.04 0.87 1.72 D 1.00 1.00 0.91 0.83 0.60 0.80 1.00 1.00 0.70 0.75 0.50 0.65 Ng/n 8 7 3 3 12 Ng 397 8 4 6 n 12 10 611 E

H 0.652 0.597 0.501 O

H 0.774 0.714 0.510

Longitude 120°37'45.34"E 120°36'42.30"E 120°52'01.98"E 120°57'08.14"E 120°57'13.94"E

Latitude 13°58'53.79"N 14°02'07.70"N 13°43'52.84"N 13°30'52.71"N 13°31'15.29"N

A A B Clade

Flat Flat Major shape Intermediate Small branch Small branch

1.0–2.0 3.7–4.7 2.0–3.5 1.4–11.0 4.1–10.7 Depth (m)*

Code VBAT VCALA VCALB

21 22 23 No. VCAL-LianA-Coral Beach Club beach VCAL-LianB-Talin Point VCAL-LianB-Talin VBAT-Anilao-Arthur's VBAT-Anilao-Arthur's Rock VPUE-Puerto GaleraA- Giant Clam Garden VPUE-Puerto GaleraB- Coral Garden Philippins Verde Passage Verde Philippins Table 1. Continued. Table Collection site *Depth ranges are approximations. Total 240 Bulletin of Marine Science. Vol 90, No 1. 2014

Figure 2. Typical gross morphologies of H. coerulea classified in this study. Right, small-branch shape; Left, flat shape. conducted two different preliminary population genetic analyses; one excluded possible clones within the same sample and the other included all the samples (genets ≥7). These 2 patterns of genetic analysis showed no difference in either the number of major clusters or the phylogenetic relationship between samples (data not shown). Here, we report the results after excluding all possible clones. Genetic Analysis.—We calculated the number of alleles per locus (A), observed heterozygosities (HO), and expected heterozygosities (HE) under Hardy–Weinberg equilibrium (HWE), using GenAlEx V6 (Peakall and Smouse 2005) (Table 2). We calculated the inbreeding coefficientsF ( IS) using FSTAT v2.9.3.2 (Goudet 1995). When the loci deviated significantly from HWE, we used the program Micro-Checker v2.2.3 (Van Oosterhout et al. 2004), which detects any large allele dropouts, or scor- ing errors due to stuttering to determine the most probable technical cause of the departure. Tests for linkage disequilibrium were performed globally using a likelihood-ratio test with the level of significance determined by permutation (Markov chain parameters: 10,000 dememorization steps; 10,000 batches; 10,000 iterations per batch) in GENEPOP on the web. Levels of statistical significance P( < 0.05) were adjusted according to a sequential Bonferroni correction for multiple comparisons (Rice 1989). A significant linkage was found between a pair of loci Saki06 and Mayu41. The linkage disequilibrium between the two loci was not consistent among the studied populations. In the present study, we used all the seven loci to determine possible genets and used only six loci (excluding Saki06) to estimate F-statistics and to perform Structure v2.3.3 analysis (Pritchard et al. 2000). Neutrality Tests.—We tested neutrality of the markers using Lositan software

(Antao et al. 2008) with 50,000 simulations, which performs an FST-outlier analysis to identify microsatellite loci under selection. We estimated significance with a 95% confidence interval. We performed three rounds of the analysis, one across the whole data set and two within the same clades (A and B) identified by Structure analysis to avoid historical events that can affect gene flow and drift, which could potentially bias the relative influence of natural selection on the studied markers. We used both the infinite alleles model (IAM) and the stepwise mutation model (SMM). Yasuda et al.: Cryptic speciation in blue coral 241

Population Differentiation.—Population pairwise FST was estimated using an analysis of molecular variance, AMOVA implemented in Arlequin v3.5.1.3

(Excoffier and Lischer 2010). The significance of pairwise FST values was assessed after 20,000 permutations. In addition, Fisher’s exact tests of differentiation between pairs of the seven loci were performed, and significance was tested using the online tool GENEPOP (Raymond and Rousset 1995) (Markov chain parameters: 10,000 dememorization steps; 5000 batches; 10,000 iterations per batch). For both FST and the significant tests, we adjusted P-values with a sequential Bonferroni correction to reduce Type I errors. To visualize the genetic relationships among samples, we conducted a principal coordinate analysis (PCA) using GenAlEx v6.4. Genotypic Admixture Analysis.—Additional insights into the patterns of gene flow between populations were obtained using the Bayesian analysis tool in Structure v2.3.3 (Pritchard et al. 2000). The number of potential clusters K( ) among H. coerulea samples was assessed from 10 different runs, each using K value ranging from 1 to 11. For each run, uninformed priors were used with a 100,000 step burn-in period and 5,000,000 step Markov Chain Monte Carlo (MCMC) replications under the admixture ancestry model and the assumption of correlated allele frequencies among samples. Following the recommendations of Evanno et al. (2005), the ad hoc statistic ΔK, which helps determine the optimal number of clusters, was calculated based on the rate of change of the log-likelihood of the present data set between con- secutive K values. Plot data were generated using the CLUMPP software (Jakobsson and Rosenberg 2007). Because neutrality tests showed Emi20 is possibly under selection, we also ran Structure with the same conditions but excluding Emi20. Isolation by Distance.—To examine whether populations that live near each other are genetically more similar than distant populations along the Kuroshio Current, the significance of isolation by distance (IBD) was tested using the Mantel test (Mantel 1967) with the program TFPGA (Miller 1997). For three regional geographic scale analyses, we estimated the approximate geographic distance along coastal lines (avoided crossing land), and for the other wider scales, we estimated the linear straight-line distance between the sampling pairs by using Google Earth. The significance of this test was assessed with 9999 random permutations of each matrix using the same program. In addition to IBD tests for global samples, IBD was tested based on the two clusters identified by Structure analysis: (1) only within the same genetic clades and (2) only between different clades.

Results

Genetic and Genotypic Diversity.—Four microsatellite loci used in our study were nearly fixed in some samples (Table 2). Of the 611 individuals genotyped in this study, we identified 397 genets across the samples. The probability that two unre- lated individuals drawn from the same sample had the same multilocus genotype by −7 −1 chance (PID) ranged from very low (2.4 × 10 SENA) to high (3.4 × 10 Okinose), as calculated by GenAlEx v. 6.41 (Table 1). Of the 47 populations examined in this study, only 23 populations had more than seven genets within the population; therefore, 23 populations were used for the subsequent population genetic analyses. Within the 23 populations, the average number of alleles across the seven loci ranged from 2.6 (SESE, VCABA) to 6.0 (IROM), with a mean of 3.9 over all the populations. Observed 242 Bulletin of Marine Science. Vol 90, No 1. 2014 . 5 4 6 5 2 2 4 4.00 0.176 −0.131 −0.135 −0.292 −0.383 −0.077 −0.179 −0.014 TWNB 4 4 5 7 2 2 5 4.14 0.117 0.117 −0.138 −0.075 −0.180 −0.818 −0.053 −0.075 −0.071 TWNA 9 5 6 2 3 7 10 6.00 0.118 0.118 0.196 0.571 0.217 0.115 0.115 IROM −0.134 −0.038 −0.227 7 5 5 1 3 7 12 5.71 0.617 SENA −0.068 −0.286 −0.190 −0.059 −0.565 −0.016 −0.252 5 4 3 5 3 4 5 NA 4.14 0.060 0.554 0.000 0.020 0.014 0.390 SESW −0.800 3 1 3 4 3 2 2 2.57 0.022 0.299 0.250 SESE −0.060 −0.314 −0.169 −0.500 −0.099 3 3 3 3 3 2 2 2.71 0.076 0.169 0.176 0.328 0.022 0.244 SESB −0.268 −0.286 3 2 5 4 3 2 4 3.29 0.050 0.182 0.106 0.250 SESA −0.059 −0.043 −0.125 −0.016 4 3 5 3 4 3 3 3.57 0.116 0.116 0.027 0.647 0.217 SHIR −0.108 −0.043 −0.200 −0.075 3 3 4 5 3 2 2 3.14 0.026 0.000 AKA −0.091 −0.277 −0.021 −0.151 −0.077 −0.120 4 2 3 2 2 2 4 NA 2.71 0.021 0.089 0.176 0.136 0.000 −0.117 −0.117 −0.290 MIYAY 5 5 5 2 3 8 10 5.43 0.480 values and (B) number of alleles for each locus sample. Populations loci with heterozygosity deficits are marked asterisks −0.244 −0.165 −0.453 −0.233 −0.202 −0.484 −0.233 AMM* IS values

IS Mean Emi20 Kumiko02* Mayu49* Mayu41 Miho33 Saki08 B. Number of alleles Saki06 All Emi20* Kumiko02* Mayu49* Mayu41* Miho33 Saki08 Saki06* Table 2. (A) F Table A. F Yasuda et al.: Cryptic speciation in blue coral 243 2 2 3 3 4 2 3 2.71 1.000 0.196 VBAT −0.091 −0.059 −0.333 −0.475 −0.059 −0.421 5 4 5 5 2 6 6 4.71 0.057 0.510 0.184 0.250 −0.043 −0.448 −0.029 −0.067 VCALB 7 4 3 2 7 7 10 NA 5.71 0.047 1.000 0.043 0.590 −1.000 −0.204 −0.155 VCALA 3 3 4 6 5 3 4 NA NA 4.00 0.322 −0.280 −0.091 −1.000 −0.143 −0.375 VMARC* 1 2 2 5 4 2 3 2.71 0.038 0.279 0.084 0.273 −0.045 −0.188 −0.006 −0.214 VMARB 1 1 2 6 2 2 4 NA 2.57 0.105 −0.295 −0.431 −0.188 −0.165 −0.714 −0.267 VCABA* 3 4 6 9 2 4 4 4.57 0.161 −0.241 −0.299 −0.361 −0.466 −0.364 −0.295 −0.188 TWNG* 4 4 2 6 2 4 3 3.57 0.004 0.000 0.374 0.179 TWNF −0.123 −0.154 −0.215 −0.152 5 4 6 6 1 2 4 4.00 0.166 0.160 TWNE −0.097 −0.226 −0.420 −0.206 −0.056 −0.072 3 4 4 7 2 2 4 3.71 0.069 0.015 0.279 0.285 0.013 TWND −0.072 −0.020 −0.143 5 4 6 6 2 3 5 4.43 0.649 0.451 TWNC −0.174 −0.363 −0.352 −0.500 −0.037 −0.105 values IS Mean Emi20 Kumiko02* Mayu49* Mayu41 Miho33 Saki08 B. Number of alleles Saki06 All Emi20* Kumiko02* Mayu49* Mayu41* Miho33 Saki08 A. F Saki06* Table 2. Continued. Table 244 Bulletin of Marine Science. Vol 90, No 1. 2014

Figure 3. Relationship between genotypic richness, genetic diversity, and latitude. The x-axis indicates latitude (north) and y-axis indicates genotypic richness (Ng/N, black circle) and genetic diversity (HE, white square).

heterozygosities (HO) in the 23 samples averaged 0.575 and ranged from 0.371 (AKA) to 0.774 (VCALA), whereas expected heterozygosities (HE) averaged 0.536 and ranged from 0.364 (SESE and VCABA) to 0.660 (IROM). No evidence of null alleles was found using Micro-Checker. Genotypic richness (Ng/n) ranged from 0.03 to 1.00 (all different genotypes) with a mean of 0.76. Genotypic diversity D ranged from 0 (only 1 genet) to 1.0 (all different genotypes) with a mean of 0.80 for D and from 0 to 3.6 (AKA) with a mean of 1.72 for H´. Genotypic evenness ranged from 0.28 (Oura-Jinza) to 1.0 (all different genotype populations) with an average of 0.91 (Table 1). The expected heterozygosities did not change with latitude (y = 0.0004x + 0.51, r2 = 0.0006). More samples were needed to examine the statistical relationship between genotypic richness and latitude, and we did not identify a clear trend but found both high and low genotypic diversities along different latitudes (Fig. 3). Neutrality Tests.—Neutrality tests across all samples under both the IAM and the SSM models showed locus Emi20 to be a candidate for positive selection. Possible selection found in Emi20 arose from large genetic difference between the clades, as neutrality tests within the same genetic clades showed that no loci were under selection for both the IAM and the SSM model. In addition, the Structure analyses both including and excluding Emi20 showed similar results (Fig. 4, Online Fig. 1). Therefore we confirmed that Emi20 alone is not responsible for the two genetic clades and including Emi20 in the analyses does not change the main conclusions in the present study. Population Differentiation and Structuring.—For the 362 genets from 23 samples analyzed here, the Structure analysis without any prior probability revealed K = 2, when the largest peak of ΔK value was detected [ΔK(2) =1714.2, ln P(D) = –5750.2 ± 0.7] (Fig. 4). Samples mostly consisting of clade A (black in Fig. 1) included AMM, SENA, IROM, the seven Taiwanese samples, and the two VCAL samples; samples mostly consisting of clade B (white) included AKA, SHIR, MIYAY, SENW, Yasuda et al.: Cryptic speciation in blue coral 245

Figure 4. Delta K estimation (graph) and Structure plots based on multilocus nuclear data for H. coerulea at K = 2 (including Emi20 for above and excluding Emi20 for below). Each vertical bar represents a single individual. Bar colors represent posterior probabilities of identity to inferred genotypic cluster. The numbers below correspond with the population numbers (see Table 1).

SESE, SESA, SESB, VMARB, VMARC, and VBAT samples (Fig. 1, Table 3). All but nine individuals across all samples were highly assigned (>90%) to either the A or B clusters, indicating limited genetic exchange between different genetic clades (Fig. 4). The mean values of the ancestry probabilities (Q) of each sample in the two clusters are summarized in Table 3. Within each population, individuals from all but three samples were highly assigned (with >90% probability) to only one of the two clades, suggesting that each H. coerulea population examined here mainly comprised a single clade. The result of global PCA analysis showed that the first two principal components explained 71.9% of the total variation in allele frequency (Fig. 5). The PCA output, especially the first axis, roughly grouped the samples into two groups, i.e., clades A and B, consistent with the results of the Structure analysis (Fig. 4). The distributions of two genetic clades (A and B) mutually overlapped along the Kuroshio Current (Fig. 1, Table 1). Both clades A and B were distributed in both Japan and the Philippines. The mean pairwise FST values within each clade were 0.120 for clade A and 0.146 for clade B.

Global FST value across all the samples 0.267, and pairwise FST values ranged from

–0.007 to 0.499 (Table 1). Pairwise FST and the result of the exact test for population differentiation showed that most sample pairs were significantly differentiated after a sequential Bonferroni correction (P < 0.05; Table 4). We found all the sample pairs between clades A and B to be significantly differentiated with high FST values (mean 0.303) and detected only six geographically close population pairs within the same clade to be non-significantly differentiated before a sequential Bonferroni correction

(P > 0.05) in both FST and exact test (shaded pairs in Table 4). Between the different Structure clades, a pairwise sample of SENA (clade A) and SENW (clade B), which are geographically separated by only 1.1 km, was significantly differentiatedF ( ST = 0.165) after a sequential Bonfferoni correction. 246 Bulletin of Marine Science. Vol 90, No 1. 2014

Table 3. Proportions of each assignment clades for 23 Heliopora coeulea populations calculated by Structure.

Clades Population n A B AMM 24 0.99 0.01 MIYAY 15 0.01 0.99 AKA 49 0.01 0.99 SHIR 30 0.05 0.95 SESA 20 0.01 0.99 SESB 20 0.01 0.99 SESE 20 0.01 0.99 SENW 26 0.05 0.95 SENA 15 0.92 0.08 IROM 15 0.94 0.06 TWNA 10 0.99 0.01 TWNB 10 0.99 0.01 TWNC 10 0.99 0.01 TWND 10 0.99 0.01 TWNE 10 0.99 0.01 TWNF 10 0.99 0.01 TWNG 10 0.99 0.01 VCABA 13 0.04 0.96 VMARB 9 0.06 0.94 VMARC 7 0.25 0.75 VCALA 11 0.89 0.12 VCALB 8 0.87 0.13 VBAT 10 0.02 0.98

All the Taiwanese samples belonged to the same clade A. Some pairwise FST values among Taiwanese samples were significant, albeit relatively low (averaged FST value = 0.053). We found both clade A and clade B in the Yaeyama and Verde Island Passage region, whereas only clade A was found in the Taiwan region

A significant but weak positive correlation between genetic FST ⁄ (1 – FST) and geographic distances was detected across all pairs of samples (Mantel test, P = 0.013, r2 = 0.047, n = 253, Fig. 6). However, within-clade IBD analyses yielded much higher r2 and more significant P values (r2 = 0.514, P < 0.001 for clade A and r2 = 0.198, P < 0.0006 for clade B) than were recovered for all pairwise samples (Fig. 6). On the other hand, no significant positive correlation between geographic distance and genetic distance of sample pairs with different clades and high intercept value (0.459) suggested that gene flow between different clades was restricted, regardless of the geographic distances.

Discussion

In the present study, we show cryptic speciation, limited gene flow with IBD patterns, and occasional low genotypic diversity in the reef-building coral H. coerulea along the Kuroshio Current. Based on the multilocus tests, three samples significantly deviated from HWE after Bonferroni correction (P < 0.05), which could be due to heterozygosity deficit (Table 2) possibly caused by deviation from panmixia. Yasuda et al.: Cryptic speciation in blue coral 247

Figure 5. Plot of principal component PC1 vs PC2 based on principal coordinate analysis. The first 2 components explained 71.9% of the data (1st 55.4% and 2nd 16.6%).

Figure 6. Pairwise FST values plotted against geographic distance. Closed triangles indicate clade A and A pairs, white circles indicate clade B and B pairs, gray squares indicate clade A and B pairs.

Multiple probability tests across all individuals indicated that all but Saki06 and Miho33 are responsible for this significant heterozygote deficiency. Cryptic Speciation Found in H. coerulea.—Structure analysis as well as PCA uncovered that the two different genetic clusters that were distributed across the area sampled in our study (Fig. 4, 5). Significant IBD patterns within each genetic clade suggest two different clades are cryptic species that formed and spread along Kuroshio Current a relatively long time ago. We found stronger gene flow between samples from the same clade than between samples from different clades, irrespective of their geographic proximity. There is no obvious physical barrier between different 248 Bulletin of Marine Science. Vol 90, No 1. 2014 * * * * * * * * * * * * * * * * * * * * *

0.294 0.206 0.247 0.438 0.183 0.319 0.211 0.329 0.157 0.287 0.239 0.070 VCALB <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 * * * * * * * * * * * * * * * * * * * * * 0.286 0.178 0.223 0.358 0.185 0.348 0.210 0.325 0.171 0.320 0.219 0.019 VCALA <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 * * * * * * * * * * * * * * * * 0.034 0.283 0.230 0.188 0.262 0.416 0.241 0.367 0.275 0.318 0.220 0.343 0.301 0.179 0.139 0.049 0.462 0.006 0.015 TWNG <0.001 <0.001 <0.001 * * * * * * * * * * * * * * * * * * 0.003 0.003 0.365 0.265 0.327 0.437 0.043 0.298 0.439 0.340 0.391 0.280 0.401 0.392 0.259 0.215 0.025 TWNF <0.001 <0.001 <0.001 <0.001 <0.001 * * * * * * * * * * * * * * * * * -values for a pairwise Fisher’s exact test are given are test exact Fisher’s pairwise a for P -values 0.068 0.134 0.331 0.239 0.009 0.267 0.461 0.246 0.398 0.284 0.370 0.119 0.119 0.224 0.386 0.303 0.158 0.116 0.014 0.002 TWNE <0.001 <0.001 <0.001 * * * * * * * * * * * * * * * * * 0.342 0.255 0.300 0.024 0.480 0.039 0.247 0.067 0.402 0.055 0.306 0.369 0.233 0.367 0.335 0.195 0.018 0.171 TWND <0.001 <0.001 <0.001 <0.001 * * * * * * * * * * * * * * * * 0.335 0.230 0.265 0.399 0.003 0.222 0.402 0.055 0.285 0.372 0.083 0.082 0.035 0.212 0.378 0.327 0.170 0.104 0.022 TWNC are given below diagonal and diagonal below given are <0.001 <0.001 <0.001 ST F * * * * * * * * * * * * * * * 0.265 0.174 0.225 0.384 0.208 0.356 0.232 0.307 0.193 0.346 0.049 0.240 0.107 0.033 0.144 0.078 0.096 0.075 0.033 0.002 TWNB <0.001 <0.001 Pairwise * * * * * * * * * * * * * * * * 0.366 0.260 0.308 0.453 0.281 0.435 0.323 0.402 0.256 0.412 0.364 0.061 0.195 0.153 0.022 0.039 0.021 0.041 0.056 TWNA <0.001 <0.001 <0.001 * * * * * * * * * * * * * * * * * * * * Heliopora coerulea. Heliopora 0.259 0.167 0.218 0.336 0.176 0.316 0.210 0.293 0.155 0.317 0.203 0.082 0.114 0.050 0.143 0.092 0.112 0.182 0.049 0.113 0.217 IROM <0.001 * * * * * * * * * * * * * * * * 0.237 0.162 0.195 0.361 0.165 0.308 0.202 0.276 0.160 0.314 0.177 0.125 0.064 0.094 0.170 0.052 0.103 0.083 0.029 0.098 0.002 SENA <0.001 * * * * * * * * * * * * * * * * * * * * * * 0.438 0.345 0.343 0.429 0.332 0.499 0.395 0.471 0.341 0.474 0.239 0.258 0.287 0.226 0.266 0.183 0.240 0.228 0.216 0.239 0.452 0.298 AMM VBAT VMARC VMARB VCABA SENW SESE SESB SESA SHIR AKA VCALA TWNG TWNF TWNE TWND TWNC TWNB TWNA IROM SENA MIYAY AMM VCALB B B B B B B B B B B A A A A A A A A A A Table 4. Pairwise population differentiation in differentiation population Pairwise 4. Table B above the diagonal. Tests that are significant after sequential Bonferroni correction (α = 0.05) are denoted with *. Shading depicts pairwise comparisons between comparisons pairwise depicts Shading *. with denoted are 0.05) = (α correction Bonferroni sequential after significant are that Tests diagonal. the above and clade B). A populations dominated by the two distinct clades (clade Clade A A Yasuda et al.: Cryptic speciation in blue coral 249 * * * * * * * * * * * * * * * * * * *

0.512 0.004 0.234 VBAT <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 * * * * * * * * * * * * * * * * * * * * 0.002 0.115 0.115 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 VMARC * * * * * * * * * * * * * * * * * * * * 0.004 0.050 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 VMARB * * * * * * * * * * * * * * * * * * * * * * 0.210 0.260 0.375 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 VCABA * * * * * * * * * * * * * * * * * * * * * * 0.281 0.069 0.080 0.109 SENW <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 * * * * * * * * * * * * * * * * * * * * * 0.002 0.108 0.492 0.239 0.199 0.175 SESE <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 * * * * * * * * * * * * * * * * * * * * * 0.074 0.001 0.067 0.042 0.337 0.108 0.068 SESB <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 * * * * * * * * * * * * * * * * * * * * 0.068 0.090 0.086 0.388 0.121 0.071 0.020 SESA <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 * * * * * * * * * * * * * * * * * * * * * 0.077 0.121 0.051 0.040 0.336 0.157 0.110 0.141 SHIR <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 * * * * * * * * * * * * * * * * * * * * * 0.083 0.022 0.082 0.416 0.210 0.164 0.142 0.075 0.075 AKA <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 * * * * * * * * * * * * * * * * * * * *

0.092 0.102 0.058 0.157 0.455 0.190 0.143 0.139 0.103 0.124 MIYAY <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 AMM SENA IROM TWNA TWNB TWNC TWND TWNE TWNF TWNG VCALA VCALB MIYAY SHIR SESA SESB SESE VCABA VMARB VMARC AKA SENW VBAT A Table 4. Continued. Table Clade A A A A A A A A A A A B B B B B B B B B B B 250 Bulletin of Marine Science. Vol 90, No 1. 2014 clades that are adjacent (only 1 km apart) such as SENW (clade B) and SENA (clade A), and it is difficult to attribute this genetic subdivision to larval dispersal barriers. These facts imply that even if larval dispersal is physically possible, post-settlement selection and/or reproductive barriers may maintain genetic separation (Yasuda et al. 2010). Further research on environmental factors especially those related to post- settlement selection (Zvuloni et al. 2008) and possible roles of dinoflagellates in corals (Bongaerts et al. 2010) are required to reveal the mechanism of speciation in H. coerulea. Similar kinds of localized genotypic subdivision and/or hidden speciation have been recently reported in other brooding corals, such as Pocillopora damicornis Linnaeus, 1758 (Benzie et al. 1995, Souter et al. 2009, 2010), S. hystrix (Bongaerts et al. 2010), Paramuricea clavata Risso, 1826 (Mokhtar-Jamai et al. 2011), species com- plex Acropora cytherea Dana, 1846 and Acropora hyacinthus Dana, 1846 (Ladner and Palumbi 2012), and the genus Oculina (Eytan et al. 2009). Population genetic analysis across Pocillopora spp., including Pocillopora damicornis Linnaeus, 1758 suggests that three reproductively distinct clades that are incongruent with traditional morph species exist in the eastern Pacific populations, implying that the hidden genetic barriers of P. damicornis observed in previous studies are attributable to different species (Pinzon and LaJeunesse 2011). Similarly, Ladner and Palumbi (2012) demonstrated the presence of at least 6 widespread cryptic species within two morphospecies complexes A. cytherea and A. hyacinthus. Bongaerts et al. (2010) found strong genetic subdivision of S. hystrix populations that inhabited different depths, and they associated this subdivision with symbiont lineages. However, a subsequent study showed that this strong genetic subdivision is related to physiological acclimation capacity (Bongaerts et al. 2011). Localized genetic differentiation by depth has been reported for the red gorgonian P. clavata in the Mediterranean Sea (Mokhtar-Jamai et al. 2011). Mokhtar-Jamai et al. (2011) concluded that unlike S. hystrix, depth-dependent genetic differentiation in P. clavata may not be caused by bleaching-related mortality but by other factors such as local topography, water flow, and summer thermocline. In the genus Oculina, neither nominal species nor population depth explained the variation, although a single population from a unique deep-water habitat was found to be genetically isolated (Eytan et al. 2009). In the case of H. coerulea, we found two genetic clades from similar depth ranges of 1–11.4 m and 0–10 m for clade A and B, respectively. Therefore, habitat depths are not associated with the genetic clades of H. coerulea (Table 1). Alternatively, the two genetic clades we observed might be the result of selection. For corals, many microsatellite loci may actually be under selection or may mir- ror selection by acting on linked gene regions (Slatkin 1995). However, Structure analyses both including and excluding Emi20, which is possibly under selection, showed the same results, indicating that the two clades are not an artifact of a subset of selected loci in our panel. Further examination using additional neutral genetic markers, especially sequence-based analysis and reproductive experiments, will help confirm species delimitation. Cryptic Speciation and Gross Morphology.—Our findings implied that clade A populations often appear in a small-branch shape and clade B populations often appear flat (Table 1, Fig. 2). Although our study lacks individual-based mor- phological data and many intermediate morphs and local variations in the field Yasuda et al.: Cryptic speciation in blue coral 251 sometimes obscured the relationship between genetic clade and gross morphology, typical small-branch shapes are most likely clade A and typical flat shapes are most likely clade B (Fig. 2). Therefore, gross morphology may be a good indicator for identification of genetic clades. Clonal Structure in H. coerulea Populations.—Genotypic diversity in H. coerulea varied among populations from almost monoclonal structure to highly ge- notypically divergent populations. Twenty-five of 47 populations were excluded from the population genetic analysis because of extremely low genotypic diversity and/ or low colony density (Ng < 7) (Table 1). Given that H. coerulea has separate sexes and produces larvae through internal fertilization (Babcock 1990), variable success of fertilization due to different colony densities, local current patterns, and success of larval recruitment from other populations may have resulted in the observed genotypic diversities. Because H. coerulea do not likely produce asexual larvae (Babcock 1990), the history of typhoon damage that may cause fragmentation would have in- fluenced the degree of asexual reproduction like that in other reef-building coral species (Baums et al. 2006). Such low diversity in populations together with limited larval dispersal suggested that effective sizes for these populations are already extremely small. Species with such restricted larval dispersal and cryptic reproductive barriers are generally predicted to be more vulnerable to disturbances than species with strong reef-connectivity (Noreen et al. 2009). Because localized extinctions have severe impacts over a long period of time, local conservation efforts that avoid severe decline are necessary. We could not detect a clear trend in genotypic diversity along latitude. We found that the Shiratorisaki population in Miyako (24°51´52.10˝N) and three Ooura Bay populations in Okinawa Main Island (26°31´50.0˝N) have extremely low genotypic diversity, whereas the Amami population (28°7´2.53˝N) that inhabits higher latitudes has relatively high genotypic diversity (Table 1). These results imply that genotypic diversity in the H. coerulea population may be governed not only by latitudinal differences (i.e., water temperature and intensity of light) but also by some localized environmental factors such as local current, disturbance, and topographic features. All three large populations in the Ooura Bay had very low (D = 0 – 0.23) genotypic diversities (Table 1), implying that some specific environmental factors in the Ooura Bay may promote asexual reproduction. Alternatively the founder effect and/or selec- tive sweep might have occurred in these populations (Yasuda et al. 2012). Although there are a few exceptions such as large expansive monoclonal Acropora palmate Lamarck, 1816 populations found in the Caribbean (Baums et al. 2006), such large populations with very low genotypic diversity seem to be generally rare (Shearer et al. 2009), highlighting the importance of conservation efforts for H. coerulea, which appear more vulnerable to disturbances than other reef-building corals. In the present study, the genetic diversity of H. coerulea did not decrease with latitude (Fig. 3). In contrast, five species of both broadcast spawner and brooding corals along eastern Australia showed a significant decrease in genetic diversity in Lord Howe Island populations, which inhabit a higher latitude than do the more “central” Great Barrier Reef (GBR) populations (Ayre and Hughes 2000). Similarly, lower genetic diversity of the brooding coral S. hystrix in the eastern Australian sub- tropics than that of GBR suggested that populations at the limit of species’ range have decreased genetic diversity (Noreen et al. 2009). Nakajima et al. (2010) found 252 Bulletin of Marine Science. Vol 90, No 1. 2014 no significant relationship between genetic diversity and latitude along the Kuroshio Current in Acropora digitifera (Dana, 1846). They concluded that exchange of larvae creates and maintains a high level of genetic diversity in this species. Although H. coerulea have a more limited gene flow than A. digitifera, infrequent long larval dispersal such as 30 d before settlement (Harii et al. 2002) would help maintain high genetic diversity in the peripheral populations. Conservation Implications.—Here, we showed that cryptic speciation of H. coerulea has important implications for conservation. Taken together, our results suggest that H. coerulea is much more vulnerable to local and global environmental change than previously thought. First, we found two different clades that appear to be reproductively isolated from each other among the H. coerulea populations examined in our study. Although it is still unclear how these genetic clades evolved, significant IBD patterns observed within each genetic clade suggest that they likely diverged through evolutionary time scales, indicating that we should discriminate the two different clades when defining management units. Second, gross morphology seems useful in identifying these genetic clades. While weak connectivity within the same clades would help maintain genetic diversity, most of the H. coerulea populations are genetically isolated and have self-recruiting larvae, indicating the need to focus on protecting existing adults that can maintain themselves with adequate larval supply and recruits. Third, we found that genetic diversity did not change with latitude, and genotypic diversities varied among populations. High genetic diversity in the peripheral populations found in H. coerulea is possibly “the legacy of the past” (Kadota and Misawa 2005). Because H. coerulea has low fecundity, separate sexes, and no asexual larvae, which are qualities that are comparable to those other reef- building corals such as P. damicornis (Babcock 1990), successful sexual reproduction requires the establishment of sufficient colony density with different sexes in the same area. Therefore, the population dynamics of H. coerulea in the Kuroshio Current may present substantial conservation and management challenges compared to other reef-building coral populations with greater larval dispersal.

Acknowledgments

We thank the following people for sampling: M Abe (The Nature Conservation Society of Japan), V Fuentes (University of Philippines Dilliman), S Harii (University of the Ryukyus), K Kajiwara (Miyako City), R Machida (Kumejima Sensui), R Magturo, T Nagata (Okinawa Environmental Research & Technology Centre), Y Nakano (The University of Ryukyu), K Okaji (Coral Quest), K Oki (Amami), M Ueno (Ishigaki), and K U y (University of Philippines Dilliman). This research was supported by grants from the Global Environment Research Fund (D-0802) of the Ministry of the Environment, Japan, a Grant-in-Aid for Scientific Research (A) (No. 21254002), a Grant-in-Aid for Research Fellows (No. 2002461) by JSPS (The Japan Society for the Promotion of Science), and the Shiseido Female Researcher Science Grant. We are very grateful for M Hellberg and an anonymous reviewer, as well as the guest editor, E Crandall, who kindly gave many significant and detailed comments on this manuscript.

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