Concepcion Et Al. 2014

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Bull Mar Sci. 90(1):257–275. 2014 research paper http://dx.doi.org/10.5343/bms.2012.1109

Regional population structure of Montipora capitata across the Hawaiian Archipelago

1 Pacific Biosciences, 1380 Willow GT Concepcion 1 * Rd, Menlo Park, California 94025. IB Baums 2 2 Department of Biology, The RJ Toonen 3 Pennsylvania State University, 208 Mueller Laboratory University Park, Pennsylvania 16802. Abstract.—Montipora capitata Dana, 1846 is one of 3 Hawai‘i Institute of Marine the most successful reef-building corals in the Hawaiian Biology, University of Hawai‘i, Archipelago, both in terms of geographic distribution and PO Box 1346, Kaneohe, Hawaii relative abundance. Here, we examine population genetic 96744. structure using eight microsatellite loci to make inferences * Corresponding author email: about exchange among geographical regions throughout . Hawaiian waters to inform management and conservation efforts. We collected biopsy samples n( = 560) from colonies at each of 11 islands/atolls along the archipelago in addition to Johnston Atoll, about 1328 km to the southwest. We found very few potential clones (<2%) in our sampling (551 of 560 colonies had unique multi-locus genotypes), indicating that reproduction is predominantly sexual. Likewise, significant

genetic structuring among most locations (pairwise F΄ST = 0.05 to 0.49, only two <0.10; P < 0.01) indicates that gene flow between islands is highly limited. Overall, we found four main regional genetic groupings of M. capitata within state waters, one comprised of the Main Hawaiian Islands, one off the three northwestern-most Hawaiian Islands, and two groupings encompassing the middle of the northwestern chain and Johnston Atoll. Despite the potential for extended pelagic larval development periods (>200 d), estimates of contemporary dispersal were uniformly low, with most sites being estimated at >90% self-recruitment. These data imply that the majority of M. capitata colonies found at a given Date Submitted: 3 January, 2013. island/atoll across the Hawaiian Archipelago are derived from Date Accepted: 5 December, 2013. self-recruitment, and argue for more local-scale management Available Online: 9 January, 2014. of coral reef resources than has been considered to date.

Aside from physical barriers such as the Isthmus of Panama, distance is among the most obvious isolating mechanisms in the sea (Grigg and Hey 1992, Lessios and Robertson 2006, Baums et al. 2012). The Hawaiian Archipelago, spanning a distance of approximately 2500 km with a mean distance of about 250 km separating islands, is one of the most isolated on the planet (Hourigan and Reese 1987). Bounded on either side by deep oceanic water unsuitable for coral reef organisms, the Hawaiian Archipelago also hosts one of the highest proportions of endemic marine species (Hourigan and Reese 1987, Kay and Palumbi 1987, Eldredge 2003). As isolated volcanic islands in the mid-ocean, all lineages present in Hawaii must have colonized from

Bulletin of Marine Science 257 © 2014 Rosenstiel School of Marine & Atmospheric Science of OA the University of Miami Open access content 258 Bulletin of Marine Science. Vol 90, No 1. 2014 elsewhere, which is evidence of their ancestral or occasional ability to disperse, and subsequent adaptation and evolution to a novel environment (Hourigan and Reese 1987). Thus, the Hawaiian Archipelago provides a model system for investigating the population biology and phylogeography of ecologically dominant coral reef species. Oriented nearly linearly in a northwest–southeast direction, the islands also serve as the northern limit to tropical coral reef diversity in the Pacific Ocean, separating the rest of the greater Indo-Pacific region from the cold waters of the North Pacific. Additionally, there are well-measured gradients of human impact and island age along the archipelago, with human impacts generally increasing and island age de- creasing as one moves from the northwest to the southeast (Fleischer et al. 1998, Price and Clague 2002, Selkoe et al. 2008, 2009). The islands are already considered a spectacular “natural laboratory” for the study of evolution in a suite of terrestrial species such as passerine birds (Freed et al. 1987), silverswords (Baldwin and Sanderson 1998), happy-face spiders (Gillespie 2004), and picture-wing Drosophila (Carson 1997, reviewed by Wagner and Funk 1995); but to date, marine examples of diversification within the islands include only the recent report of Hawaiian endemic limpets known locally as ‘opihi (Bird et al.2007, 2011, Bird 2011). The reason for this dichotomy is thought to be that marine species disperse better than do terrestrial ones (Kinlan and Gaines 2003), such that the isolation of the Hawaiian Archipelago has resulted in the marine fauna becoming differentiated from its Indo– West Pacific roots, but not diversifying (Hourigan and Reese 1987, Kay and Palumbi 1987). Because larvae of some coral species can persist for weeks or months through a coupled strategy of both autotrophy (via symbiotic dinoflagellates) and yolk stores (Richmond 1987a, Graham et al. 2008, Harii et al. 2010), it has long been assumed that the pelagic larvae have great potential to disperse and maintain broad species ranges (Jablonski and Lutz 1983, Babcock and Heyward 1986, Jackson and Coates 1986, Richmond 1987b, Ayre and Hughes 2000). Despite physical barriers such as ocean currents or freshwater intrusions, many studies of population genetic structure in corals have found evidence for gene flow over large geographic scales (e.g., Hellberg 1996, Ayre and Hughes 2000, Rodriguez-Lanetty and Hoegh-Guldberg 2002, van Oppen et al. 2008, Baums et al. 2012). Nevertheless, many have questioned the relationship between the duration of pelagic development and ability to disperse using data from population genetics (e.g., Bradbury et al. 2008, Shanks 2009, Weersing and Toonen 2009, Riginos et al. 2011, Selkoe and Toonen 2011) and range sizes (e.g., Lester and Ruttenburg 2005, Lester et al. 2007, Mercier et al. 2013). Additionally, several recent studies have documented local recruitment in fishes (e.g., Saenz-Agudelo et al. 2011, Beldade et al. 2012, Buston et al. 2012, D’Aloia et al. 2013) and kin associations of both fishes (e.g., Selkoe et al. 2006, Buston et al. 2009, Bernardi et al. 2012) and invertebrates (Iacchei et al. 2013). Data supporting or contradicting predictions about gene flow and range size based on life-history remain extremely equivocal in corals (e.g., McFadden 1997, Baums et al. 2005, Foster et al. 2007, van Oppen et al. 2008, Miller and Ayre 2008, Souter et al. 2009, Starger et al. 2010, Pinzon and LaJeunesse 2011, Combosch and Vollmer 2011, Forsman et al. 2013, Schmidt-Roach et al. 2013, Marti-Puig et al. 2014). Clearly, knowledge of pelagic larval duration and range size of a given species are not alone sufficient to predict its level of population differentiation. Concepcion et al.: Population structure of Montipora capitata in Hawaii 259

The broadcast spawner, Montipora capitata Dana 1846, is a dominant reef builder throughout the entire Hawaiian Archipelago. With considerable phenotypic plasticity, M. capitata is able to persist in a wide range of reef habitats and form both branching and plating morphologies, depending on environmental conditions (Todd 2008, Forsman et al. 2010). As one of the primary reef-building species in the Hawaiian Archipelago and an ecologically dominant species in lagoonal habitats throughout the archipelago, there is considerable interest in understanding its population structure for management. The only other coral for which population genetic structure has been reported across the Hawaiian Archipelago to date is Porites lobata Dana, 1846. Relatively little population structure in P. lobata was found, but with a significant pattern of isolation-by-distance among sites along the Hawaiian island chain (Polato et al. 2010, Baums et al. 2012). In the present study, we sampled the broadcast spawning scleractinian coral M. capitata throughout the entire length of the Hawaiian Archipelago, Johnston Atoll, and Kwajalein Atoll in the Marshall Islands to describe population genetic structure and infer patterns of gene flow.

Methods

Sample Collection, Processing and Genotyping.—Fragments of M. capitata (approximately 1 cm in length) were collected from 11 island/atoll localities (30 sites) spanning the entire Hawaiian Archipelago (approximately 2500 km) with a mean distance between localities of about 250 km. Additionally, samples were collected from five sites at Johnston Atoll and three sites at Kwajalein Atoll in the Marshall Islands at distances of about 800 and 2500 km, respectively, from the Hawaiian Archipelago (Table 1, Online Table 1). Samples were stored in 95% ethanol or DMSO saturated salt-buffer at room temperature (Gaither et al. 2011). DNA was extracted from all samples using a 96-well Qiagen DNeasy extraction kit according to the man- ufacturer protocol. All samples were genotyped at each of eight microsatellite loci and one nuclear intron region, atpsβ (Jarman et al. 2004, Concepcion et al. 2010). In brief, a three- primer method for fluorescently labeling PCR amplicons (following Concepcion et al. 2010) was used to amplify products from each microsatellite locus in each sample separately. Subsequently, for each sample, PCRs were combined into two pools, each containing four loci with uniquely labeled fluorescent dyes (Pool I: Mc0004, Mc0067, Mc0163, Mc0701; and Pool II: Mc0797, Mc0872, Mc0903, Mc0947) prior to sizing on an ABI-3100 Genetic Analyzer (Applied Biosystems). Electropherogram peaks were binned and named according to peak size with GeneMapper 4.0 (Applied Biosystems). Because computational phasing of a diploid nuclear locus is cheaper, more efficient, and can be just as accurate as cloning (Harrigan et al. 2008), nuclear locus atpsβ was amplified and sequenced directly n( = 501) following Concepcion et al. (2010). To jointly analyze locus atpsβ with the microsatellite data set, the atpsβ haplotype phas- es were determined with Phase (Stephens et al. 2001, Stephens and Donelly 2003) as implemented in DnaSP v.5.0 (Librado et al. 2009). Calculations were carried out over 1000 iterations, with a 10-iteration thinning interval, and 1000 burn-in iterations. There were 63 variable sites and we used a probability threshold of 90%. All subsequent analyses were performed assuming an infinite allele model (IAM; Kimura and Crow 1964). For this reason and due to non-stepwise microsatellite repeat spacing, 260 Bulletin of Marine Science. Vol 90, No 1. 2014

we calculated overall genetic differentiation using GST (Nei 1983) and an estimator of actual genetic differentiation Dest (Jost 2008). Overall results and interpretation of patterns were similar when using RST (Slatkin 1995) or θ (Weir and Cockerham 1984). Genotypic identities (“clones”) were assigned to individuals by calculating a pairwise matrix of genetic distances for each pair of individuals. Any samples collected at the same site that shared identical multi-locus genotypes (MLG) were collapsed into clones as implemented in GenoDive (Meirmans and Van Tienderen 2004). All subsequent analyses were performed on this data set of unique MLGs. Mantel tests and principal component analyses were also performed with GenoDive. Microchecker was used to detect null alleles (van Oosterhout 2004). Fstat v2.9.3 (Goudet 1995) was used to calculate all summary statistics as well as to test for genotypic disequilibrium and departures from Hardy-Weinberg Equilibrium (HWE). A log-likelihood ratio G-statistic was calculated for each pair of genotypes at each locus indepen- dently, allowing for inference of overall genotypic disequilibrium for each pair of loci. Hardy-Weinberg equilibrium was tested per population and per locus by examining significance of FIS values after correcting for a false discovery rate (Benjamini and Yekutieli 2001). Estimates of the population parameter θ per population were calculated in Arlequin 3.1 (Excoffier et al. 2005) according to Ewens (1972). The web service SMOGD (Crawford et al. 2009) was used to calculate global Dest, GST, and

G΄ST as well as pairwise matrices of each per locus (Hedrick 2005, Meirmans 2006,

Jost 2008). Pairwise corrected FST, F΄ST (Meirmans 2008) values were also calculated with GenoDive and significance assessed with 100,000 permutations of the data.

After Bonferroni correction, pairwise comparisons of F΄ST between all sampling sites yielded significant results only when comparing sites from different localities (Online Table 2). Because of the inferred similarity of sites within localities, sites were then lumped by locality. Mantel tests indicated a high degree of correlation 2 between pairwise estimates of G΄ST and Dest (R = 0.99, P < 0.001), and the correlation 2 between FST, F΄ST, and either Dest or G΄ST, was identical and significant R( = 0.80, P < 0.001). Likewise, an identical significant correlation was found between pairwise 2 estimates of RST and either Dest or G΄ST (R = 0.84, P < 0.001). Thus for subsequent analyses we present one uncorrected (GST) and one corrected (Dest) metric following the recommendation of Bird et al. (2011). Population Structuring and Gene Flow.—Using a Bayesian framework, population structure was explored by examining locality specific patterns of genotypic disequilibrium and subsequent clustering of lineages among an a priori de- fined number of clusters (K) as implemented in Structure 2.3.1 (Pritchard et al. 2000, Hubisz et al. 2009). For each value of K {1..15}, 20 iterations were run for 1,000,000 steps with a burn-in of 100,000. After choosing the best value of K = 4 using the ΔK statistic of Evanno et al. (2005), 10 iterations were run at this K with a chain length of 2,000,000 steps and a burn-in of 500,000. CLUMPP (Jakobsson and Rosenberg 2007) was then used to infer the optimal alignment based on these 10 iterations followed by Distruct (Rosenberg 2004) for presentation of ancestry coefficients, Q. Spatial identi- fication of barriers to gene flow were assessed using Barrier 2.2 to create a map of the Hawaiian archipelago via Delaunay triangulation and Voronoi tessellation (Manni et al. 2004). The default settings were used to plot up to three barriers (based on K = 4 groupings) using pairwise matrices of GST and Dest. Migration was also assessed with BayesAss+ (Wilson and Rannala 2003) running a MCMC consisting of 3,000,000 Concepcion et al.: Population structure of Montipora capitata in Hawaii 261 steps (with a burn-in of 1,000,000) sampled every 2000 steps. Convergence of the BayesAss+ MCMC chain was assessed by repeating the analysis with different seeds and checking for congruence. Meirmans (2012) points out that population structure can create patterns similar to IBD: if gene flow was significantly restricted among the four clusters detected by Structure and Barrier here, we would have expected a spurious IBD relationship because sites within each cluster were closer to one another than they were to sites in other clusters. On the other hand, if stepping-stone gene flow dominates the system, then this can lead models that look for hierarchical structure (such as Structure and Barrier) to find clusters where none exist (Meirmans 2012). To test for isolation-by- distance (IBD) in the Hawaiian Archipelago, both standard Mantel tests as well as stratified Mantel tests (Meirmans 2012) were performed on genetic distance and log- transformed geographic distance matrices calculated as the distance between two GPS coordinates in kilometers using the WGS84 datum (NIMA 2000).

Results

Genetic Diversity.—Out of 560 samples, 551 were unique multi-locus genotypes (MLGs; Table 1). All of the repeated MLGs had sequential sample numbers and were inferred to be redundant sampling of the same colony. Because of our interest in investigating the contribution of sexual reproduction to overall population structure, only the 551 unique MLGs were considered for further analyses. Thirty-one alleles were detected at locus atpsβ while the number of alleles for microsatellite loci ranged from 2 to 17. Expected heterozygosity across all loci for each population ranged from 0.55 to 0.72, while observed heterozygosity ranged from 0.43 to 0.58 (Table 2). Significant genotypic disequilibrium was not detected in any more than three out of 13 localities for each locus-by-locus comparison with the exception of Mc0163 and

Table 1. Regional sampling localities. Number of individuals collected (N), number of genotypes detected (Ng) and genotype to sample ratio (Ng:N) for each locality are given.

Region/locality Site code N Ng Ng:N Northwestern Hawaiian Islands Kure Atoll KU 51 51 1.00 Midway Atoll MI 43 40 0.93 Pearl and Hermes PE 44 44 1.00 Lisianski LI 50 48 0.96 Laysan LA 48 46 0.96 Maro reef MA 47 47 1.00 French Frigate Shoals FF 40 40 1.00 Nihoa NI 25 24 0.96 Main Hawaiian Islands Oahu OA 51 51 1.00 Maui MU 47 47 1.00 Hawaii HA 47 47 1.00 Johnston Atoll JO 46 46 1.00 Indo-Pacific region Kwajalein, Marshall Islands KW 21 20 0.95 Total 560 551 0.98 262 Bulletin of Marine Science. Vol 90, No 1. 2014

Table 2. Expected heterozygosity (He), observed heterozygosity (Ho), inbreeding coefficient (Fis), and θh for each locality. Fis values in bold are significant after correction for FDR (α = 0.015). See Table 1 for site codes.

Site code n He Ho Fis θh KU 51 0.55 0.46 0.077 1.216 MI 40 0.58 0.50 −0.049 1.354 PE 44 0.59 0.49 0.094 1.430 LI 48 0.57 0.43 0.177 1.345 LA 46 0.59 0.51 0.039 1.414 MA 47 0.59 0.47 0.123 1.458 FF 40 0.59 0.45 0.085 1.428 NI 24 0.64 0.47 0.115 1.762 OA 51 0.62 0.49 0.085 1.629 MU 47 0.56 0.55 −0.228 1.281 HA 47 0.58 0.58 −0.016 1.390 JO 46 0.58 0.46 0.130 1.387 KW 20 0.72 0.58 0.098 2.516

Mc0701, which showed linkage in nine out of 13 localities (Online Table 3). Locus Mc0067 and Mc0163 deviated from HWE in six and 11 populations, respectively, while all other loci showed departures from HWE in two or fewer localities (Table

2). All significantF IS values were positive, indicating a deficit of heterozygotes, which may be indicative of a Wahlund effect (Wahlund 1928), inbreeding or null alleles. Excluding the three loci mentioned above did not change the overall interpretation of our data so they were included in all subsequent analyses. Estimated values of θ per locality in Hawaii ranged from 1.216 to 1.762, while estimates for Kwajalein (θ = 2.516) were nearly twice as large (Table 2). Population Differentiation.—Almost all pairwise comparisons of both uncorrected GST and corrected G΄ST among combined sites were significant even after Bonferroni correction. Pairwise genetic distances between Hawaiian localities and

Johnston Atoll ranged from 0.006 to 0.149 and 0.026 to 0.218 for GST and Dest, respectively (Table 3). Kwajalein, the most geographically and genetically distant locality was the outlier in pairwise comparisons with genetic distances ranging from 0.053 to 0.149 and 0.196 to 0.440 for GST and Dest, respectively (Table 3). To explore the finer scale genetic structure in the Hawaiian Archipelago, Kwajalein was omitted from subsequent analyses due to its genetic and geographic isolation, and the potential for bias in analyses that assume all populations have been sampled. Population Structuring and Gene Flow.—Principal component (PC) 1 and 2 of the principal component analyses on allele frequencies among localities together explained 53.5% of the variance, sorting localities into three groups (Fig. 1). Adding a third component, PC 3, accounted for 68.3% of the variance and identified a fourth group representative of the three northern-most Hawaiian Islands/atolls. Structure analyses indicated a strong modal value of K = 4 for ΔK (Online Fig. 1; Evanno et al. 2005). Sequentially excluding each locus that showed departure from HWE, linkage disequilibrium, or both (Mc0067, Mc0163, and Mc0701) resulted in the same clustering with the same modal value of ΔK. Therefore, all nine loci were included for all analyses. Structure plots identified two clusters of lineages Concepcion et al.: Population structure of Montipora capitata in Hawaii 263 (below KW 0.281 0.066 0.019 0.141 0.084 0.109 0.035 0.037 0.101 0.098 0.098 0.120 0.120 ST G – JO 0.119 0.171 0.164 0.143 0.104 0.185 0.139 0.168 0.124 0.188 0.204 0.149 – HA 0.179 0.122 0.162 0.182 0.176 0.187 0.173 0.163 0.075 0.080 0.109 0.140 – MU 0.110 0.116 0.163 0.153 0.141 0.142 0.129 0.134 0.103 0.049 0.109 0.127 – OA 0.177 0.138 0.145 0.137 0.155 0.124 0.130 0.124 0.060 0.039 0.057 0.125 – NI 0.105 0.162 0.060 0.151 0.098 0.131 0.026 0.060 0.063 0.086 0.095 0.053 – FF 0.076 0.157 0.039 0.163 0.036 0.088 0.006 0.068 0.086 0.095 0.078 0.096 – MA 0.110 0.182 0.159 0.079 0.069 0.048 0.069 0.058 0.086 0.094 0.045 0.136 – LA 0.112 0.155 0.199 0.105 0.190 0.040 0.019 0.047 0.087 0.097 0.095 0.101 estimates based on an FDR corrected α = 0.0006. Localities specified by two letter site code found in Table in found code site letter two by specified Localities 0.0006. = α corrected FDR an on based estimates ST G – LI 0.117 0.218 0.158 0.129 0.045 0.105 0.095 0.068 0.079 0.103 0.042 0.179 – PE 0.036 0.087 0.086 0.054 0.058 0.014 0.025 0.067 0.085 0.081 0.070 0.102 – MI 0.092 0.046 0.086 0.122 0.098 0.089 0.077 0.070 0.054 0.064 0.090 0.124 – KU 0.066 0.019 0.141 0.084 0.109 0.035 0.037 0.101 0.098 0.098 0.120 0.120 KU MI PE LI LA MA FF NI OA MU HA JO KW Table 3. Distance estimates for Table pairwise comparisons of allele frequences from 13 D sampled estimator localities. (above Jost’s diagonal) and Nei’s 1 . Site code diaganol). Bold indicates significance for pairwise for significance indicates Bold diaganol). 264 Bulletin of Marine Science. Vol 90, No 1. 2014

Figure 1. Principal component (PC) analysis showing the relationship between Montipora capitata sampling localities based on allele frequencies at each site. Data points are plotted against PCs 1 and 2 and color-coded according to the relative value of PC 3 as shown in the legend. Three groupings (solid lines) can be detected based on PC 1 and PC 2 alone while color- coding the chart for PC 3 reveals the fourth grouping (dashed line) consisting of outlier localities from groups I and II. at opposite extremes of the archipelago with two additional clusters of lineages in the central portion (Fig. 2A). One of the central clusters was found at Lisianski and Johnston Atoll, while the other central cluster was found at Laysan, Maro, French Frigate Shoals, and Nihoa. Maro, French Frigate Shoals, and Nihoa were home to individuals with mixed ancestry. Clusters of individuals identified using Structure were similar in geographic location to those identified from the principal component analysis (Figs. 1, 2A).

Pairwise measures of genetic differentiation G( ST and Dest) revealed four major groupings separated by three potential barriers that were consistent with the groupings identified by both PCA and Structure (Fig. 2A, Table 3). The first grouping separated individuals at Kure, Midway, Pearl and Hermes, Lisianski, and Johnston from the rest of the archipelago. The second grouping resulted in isolation of individuals from Kure, Midway, and Pearl and Hermes, while the third isolated individuals on Oahu, Maui, and Hawaii. These groupings divided the archipelago into the same regional groups as the regional lineage clustering identified from both PCA and Structure. All analytical approaches gave similar results and thus the archipelago was divided into four proposed management units: Group I—Main Hawaiian Islands (Oahu, Maui, Hawaii); Group II—central NWHI (Nihoa, French Frigate Shoals, Maro, Laysan); Group III—Stepping Stone (Johnston, Lisianski); and Group IV— Extreme NWHI (Kure, Midway, Pearl and Hermes) (Fig. 2B). Concepcion et al.: Population structure of Montipora capitata in Hawaii 265

Figure 2. Population structuring of Montipora capitata in Hawaii. (A) Structure bar plot of Q values for each individual from every sample site and locality in Hawai’i and Johnston Atoll. Short thin black lines separate sample sites within localities denoted by longer thick black lines. Localities are labeled based on code from Online Table 1. (B) Proposed management units inferred based on consensus from PCA, Structure bar plot, and barriers to gene flow (dashed lines) inferred from Barrier.

Standard Mantel tests indicated a significant increase in genetic differentiation 2 2 with increasing geographic distance (GST: R = 0.079; G΄ST: R = 0.153; P < 0.01; Dest: R2 = 0.291; P < 0.01) (Online Fig. 1) exhibiting the signature of IBD. However as previously mentioned, patterns of population structure and IBD are often difficult to differentiate. To evaluate these alternatives, we performed a stratified Mantel test in GenoDive and found that the IBD was no longer significant when controlling for the 2 2 presence of the clusters (GST: R = 0.153; P = 0.229; Dest: R = 0.291; P = 0.215) provid- ing support for the population clusters rather than true IBD in this system. Contemporary rates of migration as estimated by BayesAss+ indicated six localities in the Hawaiian Archipelago as primarily self-seeding with the remaining six receiving some migrants from localities no more than two steps away (Table 4). Even sites that received migrants from other island locations were largely self-seeding (>67%), with no more than 30% of recruits in any site being likely to be assigned to a different island location. When considered as four population clusters (Groups I, II, III, and IV) rates of self-recruitment were estimated to be >97% for each grouping with no detectable recruitment from the other groups.

Discussion

Montipora capitata is one of the most abundant scleractinian corals across the Hawaiian Archipelago, and is known for its high reproductive output and predict- able spawning behavior in Hawaii’s waters (Kolinski 2004, Cox 2007). During the 266 Bulletin of Marine Science. Vol 90, No 1. 2014 summer spawning season, M. capitata is the most abundant coral recruit found on settlement plates in Kaneohe Bay, with the highest rates of larval settlement occurring during the first 6 wks post-spawning (Kolinski 2004). Although larvae are com- petent to settle shortly after release (approximately 3 d post spawning), they have the capacity to survive in the plankton for at least 7 mo before settling (Kolinski 2004). This extended pelagic capacity has long been assumed to result in broad dispersal that is responsible for colonization of the entire Hawaiian Archipelago by the species (Babcock and Heyward 1986, Richmond 1987b, Kay and Palumbi 1987). The paradigm of extensive dispersal among marine systems has changed, however, as more and more studies find evidence of local recruitment or fine-scale population structuring of marine species with high potential for dispersal (e.g., Bernardi et al. 2001, Swearer et al. 2002, Jones et al. 2005, Almany et al. 2007, Zvuloni et al. 2008, Timmers et al. 2012). Similarly, we provide evidence here of considerable genetic differentiation among sites for the coral M. capitata despite its potential for long distance dispersal. Local Population Genetic Structure.—Off Hawaii, a multispecies comparison of population genetic structure of marine organisms concluded that dispersal is limited among island groups (Toonen et al. 2011). Despite shorter geographic distances among islands in the Main Hawaiian Islands, three of the five barriers to gene flow within the archipelago were detected in the Main Hawaiian Islands, and the most significant break was found between the Main Hawaiian Islands and Northwestern Hawaiian Islands (Toonen et al. 2011). Montipora capitata is largely consistent with that general trend; genetic clustering separates the Main Hawaiian Islands from the adjacent Northwestern Hawaiian Islands, and the middle from the far Northwestern Hawaiian Islands (Fig. 2), but does not subdivide the Main Hawaiian Islands. However, despite the overall clustering of sites into four regional groups, we also see evidence for barriers to gene flow at finer scales; each of the islands that we sampled in the Main Hawaiian Islands are significantly differentiated from one -an other (Tables 3, Online Table 2), and show a predominance of self-recruitment (Table 4). Storlazzi et al. (2006) used acoustic doppler profiles to predict larval dispersal of corals within the Maui-Nui island complex (Maui, Lanai, Molokai, and Kahoolawe). They concluded that various regions of islands will show different degrees of reten- tion due to a variety of factors such as coastal morphology, winds, and currents. For example, their numerical modeling results suggest that the Kihei area we sampled was more retentive than west Maui (which we did not sample). These simulations showed consistent directional particle flow from west Maui to Lanai S( torlazzi et al. 2006). These data suggest that even within islands, there may be differing degrees of self-seeding and export. We cannot address this potential with the island scale at which we sampled, but our data clearly indicate that on average, most larvae of the reef-building coral M. capitata are self-recruiting at the scale of islands. This self- recruitment results in most sites along the Archipelago being significantly differentiated from one another and island-by-island population structure. Because this species is capable of reproducing both sexually and via fragmentation, the relative contribution of both modes of reproduction to local population structure must be considered (Baums et al. 2006, Severance and Karl 2006, Baums 2008). Nishikawa et al. (2008) used three allozyme loci to examine the potential contribution of clonal structure in Kaneohe Bay; finding high levels of genotypic Concepcion et al.: Population structure of Montipora capitata in Hawaii 267 diversity among samples, they concluded that sexual reproduction was the predominant mode. Our study, using a suite of eight polymorphic microsatellites and one nuclear intron locus, has much greater power, but draws the same conclusion: we identify 551 unique genotypes (Ng) of 560 total individuals sampled (n) in the data set. The ratio of gN :N was equal to one at almost every locality, and the few excep- tions were always found within collections from the same locality, these are likely caused by accidental redundant sampling of a particularly large fragmented colony or clonal reproduction. Regardless of the mechanism, with fewer than 2% of samples being genetically indistinguishable, we are confident that fragmentation is not the predominant mode of reproduction at any site we have sampled to date. Regional Division.—Multi-locus genotype analysis of an average of 43 individuals from each of 13 localities (n = 560) spanning the entire length of the Hawaiian Archipelago and including Johnston Atoll indicated at least three major geographic groupings within the Hawaiian Archipelago, and a fourth group linking Johnston Atoll to the central Northwestern Hawaiian Island grouping (Fig. 2). Some ambiguity exists, however, within the central portion of the archipelago and the link to Johnston Atoll with many individuals of mixed ancestry in this region (Fig. 2). This ambiguity could be explained by either the poor fit of the model to the underlying genetic architecture, population overlap, gene flow between the northern and southern groupings and/or Johnston Atoll, or some combination of each. The stepwise nature of dispersal along the island chain and distribution of allele frequencies along the archipelago also introduces uncertainty because the underlying model is not well suited to defining clusters in an isolation-by-distance scenario due to the way the algorithm attempts to model allele frequencies based on the weighted averages of K components (Structure manual v2.3.1, accessed 2012). Because of this poor fit of the underlying model, the inferred value of K may not be biologically or geographically relevant because many individuals in a population may have mixed member- ship in multiple groups (JK Pritchard, Stanford University, pers comm). Regardless, our analysis recovered four well-supported geographic groupings, and multivariate deconstruction of principal components, a Barrier analysis and contemporary migration estimates all corroborated these groupings on a larger scale. Thus, this degree of regional population structuring in the Hawaiian Archipelago was robust, but pairwise comparisons clearly supported additional fine-scale population structure within each cluster, down the to scale of islands, as outlined above. Similarities with Porites lobata.—The only other study of population genetic structure in a Hawaiian coral to date encompasses a similar geographical distribution and sample size for the massive coral P. lobata (Polato et al. 2010, Baums et al. 2012). Both M. capitata and P. lobata are gonochoric broadcast spawners with pelagic larvae capable of prolonged pelagic dispersal (>100 d), so at first glance we would expect similar patterns of population structuring in both. Like previous findings from P. lobata (Polato et al. 2010, Baums et al. 2012), we found that populations from extreme ends of the archipelago were isolated from one another and experienced restricted gene flow relative to geographically more proximate islands. In contrast to the findings of Polato et al. (2010) withP. lobata, however, our IBD appeared to be a result of regional population structure. We found considerably greater subdivision among islands, and a predominance of self-recruitment for M. capitata at each island along the chain within the Hawaiian Archipelago. Self-recruitment of corals 268 Bulletin of Marine Science. Vol 90, No 1. 2014 has been postulated in some species (e.g., Sammarco and Andrews 1989, Goff-Vitry and Rogers 2005, Nishikawa et al. 2005, Thomson and Frisch 2010), but to the best of our knowledge, this is the first evidence of local recruitment within island groups for a broadcast spawning coral with long-term pelagic larvae. Gene Flow and Implications for the Future.—As ever more studies emerge on fine-scale connectivity among marine species, it has become evident that self- recruitment is much more common than previously thought, and may be a major force contributing to pronounced population structure for many species in the sea (reviewed by Sponaugle et al. 2002, Strathmann et al. 2002, Swearer et al. 2002, Pinsky et al. 2012). Despite the expectation of considerable dispersal based on a maximum pelagic duration of >200 d, we found significant differentiation of most sites in our sampling. Further, there was evidence of extremely limited exchange of genetic information among localities based on estimates of contemporary migration rates from multi-locus genotypes. Migration was clearly limited to occurring within regional groupings, and primarily occurred around the same island on which the adults were located. So far as we can detect with our data, migration never occurred among islands with more than one locality separating them, although over evolutionary time long-distance dispersal must have occurred for the species to colonize the archipelago. Still, these results are consistent with shorter mean dispersal than previously thought, and hint that there may be finer-scale structure yet to be discov- ered in the Hawaiian Archipelago. In conclusion, given the genetic differentiation and low contemporary estimates of migration rates among sampling localities across the Hawaiian Archipelago, the realized dispersal of M. capitata is considerably lower than expected based on life history. If a localized population of M. capitata was to be extirpated, we estimate population recovery would proceed at best on the scale of decades, aided by recruitment of larvae from geographically proximate populations, likely on the same island as opposed to from neighboring islands. In contrast, if an entire geographical region was to be wiped out, the time required for the population to recover may be orders of magnitude longer because we detect no gene flow among geographic regions in our data, indicating that it would require geologic time scales for recovery. This latter scenario would be expected to result in contemporary ecological changes in community structure and overall biodiversity. This estimation of recovery times may seem aca- demic, but with several recent outbreaks of corallivorous crown-of-thorns sea stars (e.g., Timmers et al. 2012), and fatal coral syndromes affectingM. capitata throughout Hawaii (e.g., Aeby 2006, Aeby et al. 2010, 2011), the potential for these corals to recover from a massive die-off is of considerable management interest. Despite the prolonged larval duration of M. capitata, we find evidence of little to no successful dispersal among sites. This indicates that each island in the chain likely stands alone in responding to future climate change, coral disease, or anthropogenic insults, but also raises the possibility of local adaptation of individuals to varying habitats along the length of the Hawaiian Archipelago. Likewise, the Papahānaumokuākea Marine National Monument is an important asset for the continued preservation of biodiversity in the Hawaiian Archipelago, but cannot be regarded as a refugium for natural replenishment of impacted populations of M. capitata in the Main Hawaiian Islands. Rates of exchange between the Main Hawaiian Islands and Northwestern Hawaiian Islands are, at best, so low as to be irrelevant to management. Management Concepcion et al.: Population structure of Montipora capitata in Hawaii 269 and conservation of M. capitata, and other marine species with varying dispersal strategies, appear to require a hybrid approach with both island-specific and archipe- lagic strategies encompassing a wide variety of habitats (Toonen et al. 2011). Only by considering the long-range goals, and paying close attention to the nested geographical scale of populations (Beger et al. 2014) will conservation and management goals be reached to safeguard biological diversity.

Acknowledgments

We would like to thank B Bowen, R Brainard, Z Forsman, E Franklin, S Godwin, M Iacchei, S Karl, S Kating, B Kinzie, J Maragos, N Polato, J Salerno, D Skillings, M Stat, M Timmers, D Wagner, the staff of the Papahānaumokuākea Marine National Monument, and the crew of the R/V Hi‘ialakai for sample collection, and field and laboratory assistance. We also thank members of the ToBo laboratory for their discussion, advice, and support, and the NSF- EPSCoR Evolutionary Genetics facility at HIMB. This research was funded in part by the National Science Foundation (Grant OCE-0550294 to IBB, OCE- 0623678 to RJT, and an NSF EPSCoR pre-doctoral fellowship to GTC) and the National Oceanic and Atmospheric Administration (NMSP MOA#2005-008/66882). This is contribution 1577 from the Hawai‘i Institute of Marine Biology and SOEST 9058.

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