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Genetic Traces of Recent Long-Distance Dispersal in a Predominantly Self-Recruiting Coral



Understanding of the magnitude and direction of the exchange of individuals among geographically separated subpopulations that comprise a metapopulation (connectivity) can lead to an improved ability to forecast how fast coral reef organisms are likely to recover from disturbance events that cause extensive mortality. Reef corals that brood their larvae internally and release mature larvae are believed to show little exchange of larvae over ecological times scales and are therefore expected to recover extremely slowly from large-scale perturbations.

Methodology/Principal Findings

Using analysis of ten DNA microsatellite loci, we show that although Great Barrier Reef (GBR) populations of the brooding coral, Seriatopora hystrix, are mostly self-seeded and some populations are highly isolated, a considerable amount of sexual larvae (up to ∼4%) has been exchanged among several reefs 10 s to 100 s km apart over the past few generations. Our results further indicate that S. hystrix is capable of producing asexual propagules with similar long-distance dispersal abilities (∼1.4% of the sampled colonies had a multilocus genotype that also occurred at another sampling location), which may aid in recovery from environmental disturbances.


Patterns of connectivity in this and probably other GBR corals are complex and need to be resolved in greater detail through genetic characterisation of different cohorts and linkage of genetic data with fine-scale hydrodynamic models.


Larval dispersal and reproductive population connectivity (i.e., the dispersal of individuals among subpopulations that survive to reproduce) of most marine populations is poorly understood [1], particularly for reef corals and over recent rather than evolutionary timescales [2], [3]. This limits our ability to evaluate the design and potential benefits of novel conservation and resource management strategies. Knowledge of dispersal distances and pathways over ecological time scales is important as it will provide information regarding the recovery potential of reef coral populations that have suffered mass mortality. Recovery will occur through regrowth of surviving coral colonies and colony fragments, and through new recruitment from local and external sources. However, although some adult coral colonies seem to be able to survive severe disturbances, their presence does not guarantee replenishment because fecundity in addition to adult density determines recruitment densities [4], [5]. As well as reducing adult densities, disturbances may reduce fecundity [6], [7] and may also lower survival of eggs, larvae and juveniles [8], [9]. Recruitment from external sources is therefore likely to be extremely important for recovery after severe but localised environmental perturbations.

The Great Barrier Reef (GBR) is the world's largest reef system (∼350,000 km2, of which ∼21,000 km2 consists of coral reef) and comprises ∼2,900 separate reefs [10]. Like most other coral reefs in the world it has been affected by both anthropogenic and natural disturbances. Reef corals that brood their larvae internally and release mature larvae are generally believed to show little exchange of larvae over ecological times scales [11]. It is therefore expected that such corals are extremely slow in recovering from large-scale perturbations. The brooding scleractinian (stony) coral, Seriatopora hystrix Dana 1846, is a widespread and common species on the GBR [12], and is among the most sensitive species to coral bleaching [13]. Hence, this species is under severe threat from climate change related warming and it is unclear whether damaged populations can be repopulated from external sources.

We follow a genetic approach to obtain an indirect measure of reproductive population connectivity [2], [3], [14] in S. hystrix from the central to northern GBR. Previous studies assessing the connectivity of scleractinian corals on the GBR, Australia, have either been conducted over small spatial scales [15], have involved a small number of sampling locations [16], [17], or have used small numbers of loci [18], [19] and/or allozymes rather than DNA markers [20]. Our study is based on a large sample size (1,025 colonies from 22 collection sites) and 10 DNA loci, and as a consequence reveals several new findings with respect to the temporal and spatial scale of connectivity among populations of this coral species on the GBR.


The AMOVA (Analysis of Molecular Variance) indicates a high level of genetic structuring in this coral species (22% of the total molecular variance is partitioned among populations; based on the Infinite Allele Model, p<0.0001), suggesting that most recruitment is highly localised. This is supported by the indices of pairwise genetic differentiation (mean FST = 0.201±0.125 SD), which are significant for all but 3 comparisons (i.e., Davies Rf 1 vs. Davies Rf 3, Yonge Reef vs. Rib Reef 10 and Rib Reef 5_2005 vs. Rib Reef 8; Table 1). The genetic composition of each of the populations is visualised in Figure 1, using the model-based clustering method implemented in STRUCTURE v2.2 [21] under the assumption that there are 20 genetic clusters (Figs. 2A, B). This analysis shows that the log probability of the data starts to plateau at a K (number of genetic clusters) of about 20 (Fig. 2A). As this coincides with one of the optima in ΔK (Fig. 2B), we have interpreted these results as 20 being the most likely number of genetic clusters in the data, although K = 2 and K = 7, the other two optima in ΔK, were also explored (discussed below but data not shown). The following patterns are revealed by the analysis based on K = 20 (Fig. 1) and these are generally supported by pairwise FST values (Table 1): (1) Osprey Rf in the Coral Sea and the inshore Cattle Bay are genetically the most distinct, (2) Sites within a reef are in some instances as genetically distinct as sites hundreds of km apart (e.g. the Lizard Is sites), while in other cases they have FST values not significantly different from zero (e.g., the two lagoonal sites Davies Rf 1 and 3), (3) Geographically distant sites are sometimes genetically similar (e.g., Lizard Is 2 and Agincourt Rf), (4) Populations in the Ribbon Reefs tend to be more genetically similar than populations elsewhere.

Figure 1. Genetic composition of S. hystrix at the 22 sampling sites based on the model-based clustering method implemented in STRUCTURE v2.2 [21].

Figure 2. Results of the fully Bayesian model-based clustering method implemented in STRUCTURE v2.2 [21], which was used to infer the most likely number of populations (K) in the data set.

For this purpose, the program was run without population information under the admixture model (individuals may have mixed ancestry) and independent allele frequencies. Length of the burn-in was 100,000 and the number of MCMC replications after the burn-in was 1,000,000. Five independent chains were run for each K from K = 2 to K = 26. A) Ln P for each K for K = 2–26, B) ΔK for each K for K = 2–26 [52].

Table 1. Pairwise FST values calculated using an AMOVA approach [46] in GenAlEx v6 [47].

K = 2 is unrealistically small given the high population structure in this species based on FST values obtained here and in previous work [15]. However, it does suggest that the following populations are admixed: Lizard Is 2, Ribbon Rf 5_2003, Emily Rf, Tongue Rf 2, Davies Rf 2 and Big Broadhurst Rf. The latter is consistent with the results of other analyses which are presented below. K = 7 shows similar patterns to K = 20, i.e., most of the Ribbon Reef sites are genetically similar; Cattle Bay is genetically very distinct; there is some affinity between Osprey and Ribbon Rf 5_2003, yet these two populations are distinct from all other populations; Lizard Is 2 is the most distinct of the three Lizard Is sites; Davies Rf 2 is genetically closer to Big Broadhurst Rf than to the two lagoonal Davies Rf sites; Agincourt Rf is similar to Lizard Is 2. Further, with K = 7 a weak latitudinal trend is distinguishable (which is not as obvious from the K = 20 analysis), but some geographically distant population pairs are genetically similar.

Approximately 2.5% of the samples (25) were found to have multi-locus genotypes that occur more than once. Eleven of those occur within sites and one sample of each pair was removed before analysis as it was assumed that clones within a collection site were produced through fragmentation. In addition, 14 multi-locus genotypes are shared between some of the sites sampled (Table 2). These allopatric clone mates were not removed from the data prior to analysis, since it concerns a relatively small number of specimens and because it is unknown which of the sites was the source. However, we performed the AMOVA with and without the allopatrically occurring repeated genotypes and obtained the same results.

Table 2. Locations of the 14 multi-locus genotypes found at more than one site.

An exclusion test conducted in GeneClass v2.0 [22] identifies 42 individuals (∼4%) as having originated at a site other than the site these were sampled from (Table 3). While the majority of these are likely to have been sourced from unsampled populations (i.e., these individuals have extremely low probabilities of coming from any of the sampled populations), 14 of the excluded individuals can be assigned to one or more of the other sampling locations. S. hystrix is very common and widespread on the GBR, and many potential source populations were therefore not sampled in this study. Hence, it is possible that some immigrants have been assigned to a population they have not originated from. Given that geographically close populations are generally genetically more similar than geographically distant ones (although there are a few exceptions to this pattern), however, we are confident that the estimates of the spatial scales over which these larvae have dispersed are reasonably accurate. Some of the migrants are inferred to be sourced from nearby sites (e.g., Davies Rf versus Big Broadhurst Rf), while others appear to have been transported over distances of 10 s to 100 s of kilometres (e.g., Emily Rf versus Sudbury Rf). However, there is no assignment of migrants between the extremes of the sampling range, providing further confidence that these results provide reasonably accurate estimates of recent dispersal distances.

Table 3. Results of exclusion analysis conducted in GeneClass v2.0 [22].

Significant Linkage Disequilibrium (LD) was found in 165 out of 1,035 comparisons (15.9%), and at the following sites (numbers of locus pairs in parentheses): Osprey Rf (5), Yonge Rf (1), Ribbon Rf 5_2003 (11), Lizard Island 1 (1), Lizard Island 2 (17), Emily Rf (30), Agincourt Rf (19), Tongue Rf 1 (1), Tongue Rf 2 (16), Flora Rf (5), Myrmidon Rf (4), Davies Rf 2 (25) and Big Broadhurst Rf (29). All the sites with large numbers of LD also show significant heterozygote deficits involving 3–9 loci (Fig. 3 and Table 4). However, none of the loci exhibit significant deficits at all locations, with the highest number of significant heterozygote deficits observed for Sh2-005 (8 sites), indicating that these results are unlikely to be due to the presence of null alleles. Exact tests show significant deviations from Hardy-Weinberg Equilibrium (HWE) exist in the sites at Osprey Rf (3 loci), Yonge Rf (1 locus), Ribbon Rf 10 (1 locus), Ribbon Rf 5_2003 (4), Lizard Island 1 (1 locus), Lizard Island 2 (6 loci), Lizard Island 3 (1 locus), Emily Rf (8 loci), Agincourt Rf (9 loci), Tongue Rf 2 (6 loci), Flora Rf (5 loci), Myrmidon Rf (4 loci), Davies Rf 2 (9 loci) and Big Broadhurst Rf (8 loci), while all 10 loci are in HWE at the remaining 8 sites. Presence of extensive LD and heterozygote deficits was found mainly in locations exhibiting high levels of genetic diversity, as assessed by allelic richness and expected heterozygosity (Fig. 3).

Figure 3. Genetic signature of recent admixture.

The combination of large numbers of locus pairs in Linkage Disequilibrium (LD), heterozygote deficits at a large number of loci, and high genetic diversity (HE and allelic diversity) in some of the populations is indicative of recent admixture. HE = Expected heterozygosity, Loci = number of loci with significant heterozygote deficit, Locus pairs = number of locus pairs showing Linkage Disequilibrium, Richness = allelic richness.

Table 4. Descriptive statistics for the ten microsatellite loci at 22 sites of S. hystrix.


Population structure on the GBR

This study shows a high level of genetic structuring among most GBR populations of the coral S. hystrix, supporting earlier findings based on allozyme analysis of the same species on the GBR [15], [16] and the fact that most larvae settle within several hours to days after release in laboratory studies [23], [24]. Recently, Underwood et al. [25] have demonstrated that most recruitment in NW Australian S. hystrix populations occurs within 100 m of the natal colony, a finding also supported by our data.

The extreme genetic distinctiveness of the Osprey Rf population is likely due to its geographic isolation in the Coral Sea, and that of Cattle Bay by the limited cross-shelf water exchange in the central GBR [26], [27]. Most sites in the Ribbon Reefs are genetically more similar than populations elsewhere and some exhibit pairwise FST values not significantly different from zero, consistent with an almost continuous north-south reef matrix along the Ribbon Reefs acting as a stepping stone for coral dispersal. There are, however, exceptions to this pattern. For example, the two sites sampled at Ribbon Rf 5 are very genetically divergent (Fig. 1). This may reflect habitat differences (the mean collection depth of the two sites was 8.4 m and 3.3 m), or temporal variation (the samples were collected in different years, in this case 2003 and 2005). However, because no known major disturbance events have occurred between the two sampling time points at Ribbon Rf 5, our interpretation of the results is that they reflect independent bouts of recruitment, the Ribbon Rf 5_2003 population possibly from outside the reef. This is supported by the relative genetic similarity between some geographically distant sites (e.g., Lizard Is 2 and Agincourt Rf, see Fig. 1). While theoretically this can be the result of size homoplasy of alleles, it is unlikely to be the case here as the same most common alleles are found at all loci (data not shown). Random genetic drift could also have led to similar allele frequencies, but again, it is unlikely this would have happened at all of the 10 loci examined. The most plausible explanation therefore is that a recruitment pulse has occurred from one location to the other or from another genetically similar, but unsampled location. Higher levels of genetic differentiation within rather than between reefs is commonly observed in corals and other marine organisms [16], [28], [29], and this may reflect the spatial and temporal stochasticity of larval recruitment due to complex and temporally variable patterns of water circulation around the reef matrix [27], [30] as well as temporal variation in fecundity of marine organisms [4], [5]. To better understand the stochasticity of recruitment in reef corals, future studies should focus on the genetic characterisation of different cohorts, for example by studying distinct size classes or new recruits over the course of several recruitment cycles.

Localised recruitment is supplemented by recent longer distance dispersal

Despite most recruitment in S. hystrix on the GBR being highly localised, our results suggest a considerable amount of recent exchange of both sexual and asexual larvae has occurred, and that up to ∼5.4% of the total number of colonies sampled may represent recent migrants. Fourteen multi-locus genotypes were shared between some of the sites sampled. As it is highly unlikely that coral fragments would survive transportation by water movement between reefs, these results suggest that S. hystrix occasionally produces asexual larvae and that those can be swept off the natal reef and settle elsewhere, in some instances more than 100 km away (Table 2). Only sexual larvae have been described for S. hystrix; two independent allozyme studies showed the presence of non-maternal alleles in some of the larvae from 1 colony from the central GBR [31] and 6 colonies from the southern GBR [32], indicating that the broods were sexually produced. However, reproduction is highly plastic in brooding pocilloporid corals [33], [34] and it is therefore possible that some populations produce asexual larvae. Alternatively, these results could be explained by polyp bail-out, a stress response first described in S. hystrix [35], where polyps detach themselves from the skeleton, disperse and re-attach to the substratum.

In addition to recent long-distance dispersal of asexually produced larvae, migration of sexually produced larvae can be inferred from the exclusion test results (Table 3). S. hystrix produces mature planula larvae, already containing algal endosymbionts [23], [36]. The planulae are of a range of sizes with the larger planulae having the longer survivorship, suggesting a strategy that accommodates both short and long-distance dispersal [37]. Also, we note that non-fed planulae of the related species, Pocillopora damicornis, are able to settle after 2 hrs in the laboratory, but ∼5% of the planulae can remain competent to settle for >103 days after release from the parental colony [38]. The presence of algal endosymbionts in the planulae may increase their survival as these are an important energy source [37]. Further, brooding corals, including S. hystrix, tend to show extended periods (several months) of larval release compared to broad-cast spawning corals [36], thereby increasing the probability of favourable hydrodynamic conditions for the occurrence of long-distance dispersal of larvae. Finally, it is possible that longer distance dispersal occurs through rafting of small colonies attached to floating material, such as coconuts [39].

The combination of high levels of Linkage Disequilibrium (LD), heterozygote deficits at large number of loci, and high genetic diversity in some of the populations (Fig. 3) is also indicative of recent admixture. Strong associations between physically unlinked loci are caused by the co-occurrence of alleles at different loci in the migrants and their early descendants [40]. LD among loci can be maintained for several generations and decays according to the recombination rate [41]. Dispersal of individuals among genetically distinct populations also causes heterozygote deficits due to the resulting changes in allele frequencies in the receiving population [42] and inflated genetic diversity due to the entry of new alleles into the population. This signature of recent admixture can be used to identify source and sink reefs and complements the results from the exclusion test. Recent migrants identified by the exclusion analysis occur at Osprey Rf, Emily Rf, Agincourt Rf, Tongue Rf 2, Flora Rf, Myrmidon Rf, Davies Rf 2 and Big Broadhurst Rf, consistent with a strong signature of recent admixture at these sites (Fig. 3). This suggests these reefs act as sinks, or as sinks and sources simultaneously. Near HWE has previously been found in reef lagoon populations of this species in the central GBR, but not in other habitats [15]. In the two reef areas where both lagoonal and non-lagoonal sites were sampled (Davies Rf and Lizard Is), we observed the same pattern. This suggests that lagoonal populations are generally more self-seeded than non-lagoonal populations. Based on the presence of clone mates in allopatry and the lack of a recent admixture signature, Sudbury Rf 2 is a key example of a source reef, sending migrants both north and south (Table 2). The population size of S. hystrix at the two Sudbury reefs was unusually large and the colonies on this reef were also large (MJHvO and AHL, personal observations). This suggests that a disproportionally large number of larvae are produced here and the likelihood of some larvae reaching other reefs and establishing themselves is relatively high.


In conclusion, our study is the first in which a considerable level of recent migration over spatial scales of 10 s to 100 s of km has been shown in a brooding coral on the GBR. S. hystrix is extremely sensitive to heat and light stress [13], but it seems to have the potential to recover after major disturbances. While it is possible that small colony fragments survive bleaching events in crevices, sheltered from high light levels, and regrow to form large mature colonies, the results reported here indicate that some recovery is also possible through migration from external reefs.

Materials and Methods

Sampling of corals

Small fragments from 1,025 colonies (36–54 per site) of the coral Seriatopora hystrix (Scleractinia: Pocilloporidae) were collected between March 2003 and February 2005, and their genotypes at 10 microsatellite loci were determined. The samples originated from 21 collection sites on the GBR and one site in the Coral Sea (Osprey Reef) (Fig. 1), spanning ∼5 degrees or ∼500 km of latitude. Sampling occurred over a spatial scale of ∼100 m2 at each site. The work by Underwood et al. [25] has shown that most S. hystrix larvae on NW Australian reefs settle within 100 m of natal colony, suggesting that this spatial scale is appropriate for the species. At most reefs, collections were made in non-exposed/lagoonal areas, with the exception of Lizard Island and Davies Reef, where two lagoonal and one non-lagoonal site was sampled. No sampling across habitat gradients was conducted.

Genetic characterisation of coral colonies

DNA was extracted following a slightly modified method used for the black tiger shrimp [43]. PCR amplification of the ten microsatellite loci is described in Underwood et al. [44] and was carried out in 10 µL volumes. Following PCR amplification, 5 µL were purified either by precipitation or on a Sephadex G-50 column and the products were separated on the GE Healthcare MegaBace 1000 capillary sequencer. An internal size standard (ET 400-R, GE Healthcare) was run in every sample.

Data analysis

Chromatograms were imported into the MegaBACE Genetic Profiler Software Suite version 2 (GE Healthcare) to determine the fragment sizes (alleles) present in the samples. All automatic scoring was checked manually, and samples that yielded ambiguous or no signal were re-amplified and rerun.

Genotypic Linkage Disequilibrium (LD) and deviations from Hardy-Weinberg Equilibrium (HWE) were assessed in GENEPOP (web version 3.4) by estimation of exact p-values by the Markov chain method [45]. FST values were calculated using an AMOVA approach [46] in GenAlEx v6 [47]. Statistical significance of pairwise FST values was based on 9,999 permutations. The probabilities of identity by sexual reproduction were calculated using the software package GENCLONE 2.0 [48], which implements a method that takes into account the FIS estimated from each population [49] as S. hystrix is known to show some level of self-fertilisation [32] and deviations from HWE were observed in some of the sampled populations. Statistical significance levels for all pairwise tests were adjusted for multiple comparisons using a False Discovery Rate (FDR) method [50]. Allelic richness and its statistical significance was calculated in FSTAT [51], standardized to the smallest sample following the rarefaction method.

The fully Bayesian model-based clustering method implemented in STRUCTURE v2.2 [21] was used to infer the most likely number of genetic clusters (K) in the data set. For this purpose, the program was run without population information under the admixture model (individuals may have mixed ancestry) and independent allele frequencies. Length of the burn-in was 100,000 and the number of MCMC replications after the burn-in was 1,000,000. Five independent chains were run for each K from K = 2 to K = 26. The method of Evanno et al. [52] was used to find the most likely value of K (Figs. 2 A, B).

To identify first generation migrants, an exclusion test was conducted in GeneClass v2.0 [22]. The likelihood that an individual originated from each collection location was computed following the criterion of Rannala and Mountain [53]. This likelihood was compared with the likelihood distribution of 10,000 simulated genotypes from each sampling location [54]. To obtain a conservative estimate of recent migration, an individual was excluded from its sampling site when the probability of exclusion was greater than 99% (P or α≤0.01). Potential source reefs of the excluded individuals were identified based on probabilities >0.1.


We thank the following people for their assistance in the collection of the coral samples: David Abrego, Ray Berkelmans, John Carleton, Jeremy Downs, Isabelle Fleury, Tim Hyndes, Paul Muir, Kristie Nobes, Damina Thomson, Karin Ulstrup, and Sven Uthicke. We also thank Andrew Muirhead for his help with DNA extractions and Petra Souter for valuable discussions about this work.

Author Contributions

Conceived and designed the experiments: MJHvO. Performed the experiments: AL LP. Analyzed the data: MJHvO AL GD. Contributed reagents/materials/analysis tools: MJHvO. Wrote the paper: MJHvO. Sample collection: MJHvO AL. Manuscript editing: AL GD SK. Contribution to data interpetation: AL GD SK.


  1. 1. Cowen RK, Gawarkiewicz G, Pineda J, Thorrold SR, Werner FE (2007) Population connectivity in marine systems: An overview. Oceanogr 20: 14–21.
  2. 2. van Oppen MJH, Gates RD (2006) Conservation genetics and the resilience of reef-building corals. Mol Ecol 15: 3863–3883.
  3. 3. Hellberg ME (2007) Footprints on water: the genetic wake of dispersal among reefs. Coral Reefs 26: 463–473.
  4. 4. Hughes TP, Baird AH, Dinsdale EA, Harriott VJ, Moltschaniwskyj NA, et al. (2002) Detecting regional variation using meta-analysis and large-scale sampling: Latitudinal patterns in recruitment. Ecology 83: 436–451.
  5. 5. Hughes TP, Baird AH, Dinsdale EA, Moltschaniwskyj NA, Pratchett MS, et al. (2000) Supply-side ecology works both ways: The link between benthic adults, fecundity, and larval recruits. Ecology 81: 2241–2249.
  6. 6. Michalek-Wagner K, Willis BL (2001) Impacts of bleaching on the soft coral Lobophytum compactum. I. Fecundity, fertilization and offspring viability. Coral Reefs 19: 231–239.
  7. 7. Cantin NE, Negri AP, Willis BL (2007) Photoinhibition from chronic herbicide exposure reduces reproductive output of reef-building corals. Mar Ecol Progr Ser 344: 81–93.
  8. 8. Negri AP, Marshall PA, Heyward AJ (2007) Differing effects of thermal stress on coral fertilization and early embryogenesis in four Indo Pacific species. Coral Reefs 26: 759–763.
  9. 9. Negri AP, Smith LD, Webster NS, Heyward AJ (2002) Understanding ship-grounding impacts on a coral reef: potential effects of anti-foulant paint contamination on coral recruitment. Mar Poll Bull 44: 111–117.
  10. 10. Wachenfeld D, Johnson J, Skeat A, Kenchington R, Marshall P, et al. (2007) Introduction to the Great Barrier Reef and climate change. In: Johnson JE, Marshall PA, editors. Climate change and the Great Barrier Reef: A vulnerability assessment. Townsville: Great Barrier Reef Marine Park Authority and Australian Greenhouse Office. pp. 1–13.
  11. 11. Ayre DJ, Hughes TP (2004) Climate change, genotypic diversity and gene flow in reef-building corals. Ecol Lett 7: 273–278.
  12. 12. Veron JEN (2000) Corals of the world: Australian Institute of Marine Science.
  13. 13. Marshall PA, Baird AH (2000) Bleaching of corals on the Great Barrier Reef: differential susceptibilities among taxa. Coral Reefs 19: 155–163.
  14. 14. Palumbi SR (2003) Population genetics, demographic connectivity, and the design of marine reserves. Ecol Appl 13: S146–S158.
  15. 15. Ayre DJ, Dufty S (1994) Evidence for restricted gene flow in the viviparous coral Seriatopora hystrix on Australia's Great Barrier Reef. Evolution 48: 1183–1201.
  16. 16. Ayre DJ, Hughes TP (2000) Genotypic diversity and gene flow in brooding and spawning corals along the Great Barrier Reef, Australia. Evolution 54: 1590–1605.
  17. 17. Benzie JAH, Haskell A, Lehman H (1995) Variation in the genetic composition of coral (Pocillopora damicornis and Acropora palifera) populations from different reef habitats. Mar Biol 121: 731–739.
  18. 18. MacKenzie JB, Munday PL, Willis BL, Miller DJ, van Oppen MJH (2004) Unexpected patterns of genetic structuring among locations but not colour morphs in Acropora nasuta (Cnidaria; Scleractinia). Mol Ecol 13: 9–20.
  19. 19. Miller KJ, Ayre DJ (2008) Population structure is not a simple function of reproductive mode and larval type: insights from tropical corals. J Anim Ecol 77: 713–724.
  20. 20. Smith-Keune C, van Oppen MJH (2006) Genetic structure of a reef-building coral from thermally distinct environments on the Great Barrier Reef. Coral Reefs 25: 493–502.
  21. 21. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155: 945–959.
  22. 22. Piry S, Alapetite A, Cornuet JM, Paetkau D, Baudouin L, et al. (2004) GENECLASS2: A software for genetic assignment and first-generation migrant detection. J Hered 95: 536–539.
  23. 23. Atoda K (1951) The larvae and post-larval development of some reef-building corals. V. Seriatopora hystrix Dana. Reports of the Tohoku University Fourth Series XIX: 48–65.
  24. 24. Baird AH (2001) The ecology of coral larvae: settlement patterns, habitat selection and the length of the larval phase. PhD thesis, Townsville: James Cook University.
  25. 25. Underwood JN, Smith LD, van Oppen MJH, Gilmour J (2007) Multiple scales of genetic connectivity in a brooding coral on isolated reefs following catastrophic bleaching. Mol Ecol 16: 771–784.
  26. 26. Brinkman R, Wolanski E, Deleersnijder E, McAllister F, Skirving W (2002) Oceanic inflow from the Coral Sea into the Great Barrier Reef. Est Coast Shelf Sci 54: 655–668.
  27. 27. Luick JL, Mason L, Hardy T, Furnas MJ (2007) Circulation in the Great Barrier Reef Lagoon using numerical tracers and in situ data. Cont Shelf Res 27: 757–778.
  28. 28. Johnson MS, Black R (2006) Islands increase genetic subdivision and disrupt patterns of connectivity of intertidal snails in a complex archipelago. Evolution 60: 2498–2506.
  29. 29. Whitaker K (2006) Genetic evidence for mixed modes of reproduction in the coral Pocillopora damicornis and its effect on population structure. Mar Ecol Progr Ser 306: 115–124.
  30. 30. Steinberg C (2007) Impacts of climate change on the physical oceanography of the Great Barrier Reef. In: Johnson JE, Marshall PA, editors. Climate Change and the Great Barrier Reef: A vulnerability Assessment. Townsville: Great Barrier Reef Marine Park Authority and Australian Greenhouse Office. pp. 52–74.
  31. 31. Ayre DJ, Resing JM (1986) Sexual and asexual production of planulae in reef corals. Mar Biol 90: 187–190.
  32. 32. Sherman CDH (2008) Mating system variation in the hermaphroditic brooding coral, Seriatopora hystrix. Hered 100: 296–303.
  33. 33. Tanner JE (1996) Seasonality and lunar periodicity in the reproduction of Pocilloporid corals. Coral Reefs 15: 59–66.
  34. 34. Sherman CDH, Ayre DJ, Miller KJ (2006) Asexual reproduction does not produce clonal populations of the brooding coral Pocillopora damicornis on the Great Barrier Reef, Australia. Coral Reefs 25: 7–18.
  35. 35. Sammarco PW (1982) Polyp bail-out: an escape response to environmental stress and a new means of reproduction in corals. Mar Ecol Prog Ser 10: 57–65.
  36. 36. Harrison PL, Wallace CC (1990) Reproduction, dispersal and recruitment of scleractinian corals. In: Dubinsky Z, editor. Ecosystems of the World: Coral Reefs. Amsterdam: Elsevier. pp. 133–207.
  37. 37. Isomura N, Nishihira M (2001) Size variation of planulae and its effect on the lifetime of planulae in three pocilloporid corals. Coral Reefs 20: 309–315.
  38. 38. Richmond RH (1987) Energetics, competency, and long-distance dispersal of planula larvae of the coral Pocillopora damicornis. Mar Biol 93: 527–533.
  39. 39. Jokiel PL (1984) Long distance dispersal of reef corals by rafting. Coral Reefs 3: 113–116.
  40. 40. Hartl DL, Clark AG (1989) Principles of Population Genetics. Sunderland, MA: Sinauer Associates Inc.
  41. 41. Barton NH, Gale KS (1993) Genetic analysis of hybrid zones. In: Harrison RG, editor. Hybrid Zones and the Evolutionary Process. New York: Oxford University Press. pp. 13–45.
  42. 42. Crow JF, Kimura M (1970) An Introduction to Population Genetics Theory. New York: Harper and Row Publishers.
  43. 43. Wilson K, Li Y, Whan V, Lehnert S, Byrne K, et al. (2002) Genetic mapping of the black tiger shrimp Penaeus monodon with amplified fragment length polymorphism. Aquaculture 204: 297–309.
  44. 44. Underwood JN, Souter PB, Ballment ER, Lutz AH, van Oppen MJH (2006) Development of 10 polymorphic microsatellite markers from herbicide-bleached tissues of the brooding coral Seriatopora hystrix. Mol Ecol Notes 6: 176–178.
  45. 45. Raymond M, Rousset F (1995) GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Hered 86: 248–249.
  46. 46. Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131: 479–491.
  47. 47. Peakall R, Smouse PE (2005) GenAlEx 6: Genetic Analysis in Excel. Population genetic software for teaching and research. Canberra, Australia: Australian National University.
  48. 48. Arnaud-Haond S, Belkhir K (2007) GENCLONE: a computer program to analyse genotypic data, test for clonality and describe spatial clonal organization. Mol Ecol Notes 7: 15–17.
  49. 49. Young AG, Hill JH, Myurray BG, Peakall R (2002) Breeding system, genetic diversity and clonal structure in the sub-alpine forb Rutidosis leiolepis F. Muell. Biol Cons 106: 71–78.
  50. 50. Benjamini Y, Yekutieli D (2001) The control of false discovery rate under dependency. Ann Stat 29: 1165–1188.
  51. 51. Goudet J (1995) Fstat version 1.2: a computer program to calculate F-statistics. J Hered 86: 485–486.
  52. 52. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14: 2611–2620.
  53. 53. Rannala B, Mountain JL (1997) Detecting immigration by using multilocus genotypes. Proc Natl Acad Sci, USA 94: 9197–9201.
  54. 54. Paetkau D, Slade R, Burden M, Estoup A (2004) Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation-based exploration of accuracy and power. Mol Ecol 13: 55–65.
  55. 55. Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38: 1358–1370.