Abstract

Although migratory pelagic fishes generally exhibit little geographic differentiation across oceans, as expected from their life history (broadcast spawning, pelagic larval life, swimming ability of adults) and the assumed homogeneity of the pelagic habitat, exceptions to the rule deserve scrutiny. One such exception is the narrow-barred Spanish mackerel (Scomberomorus commerson Lacepède, 1800), where strong genetic heterogeneity at the regional scale has been previously reported. We investigated the genetic composition of S. commerson across the Indo-West Pacific range using control-region sequences (including previously published data sets), cytochrome b gene partial sequences, and eight microsatellite loci, to further explore its phylogeographic structure. All haplotypes sampled from the Indo-Malay-Papua archipelago (IMPA) and the south-western Pacific coalesced into a clade (clade II) that was deeply separated (14.5% nucleotide divergence) from a clade grouping all haplotypes from the Persian Gulf and Oman Sea (clade I). Such a high level of genetic divergence suggested the occurrence of two sister species. Further phylogeographic partition was evident between the western IMPA and the regions sampled east and south of it, i.e. northern Australia, West Papua, and the Coral Sea. Strong allele-frequency differences were found between local populations in the south-western Pacific, both at the mitochondrial locus (Φst = 0.282–0.609) and at microsatellite loci (graphic = 0.202–0.313). Clade II consisted of four deeply divergent subclades (9.0–11.8% nucleotide divergence for the control region; 0.3–2.5% divergence at the cytochrome b locus). Mitochondrial subclades within clade II generally had narrow geographic distribution, demonstrating further genetic isolation. However, one particular haplogroup within clade II was present throughout the central Indo-West Pacific: this haplogroup was found to be the sister group to a haplogroup restricted to West Papua and the Coral Sea, yielding evidence of recent secondary westward colonization. Such a complex structure is in sharp contrast with the generally weak phylogeographic patterns uncovered to date in other widely distributed, large pelagic fishes with pelagic eggs and larvae. We hypothesize that in S. commerson and possibly other Scomberomorus species, philopatric migration may play a role in maintaining the geographic isolation of populations by annihilating the potential consequences of passive dispersal.

INTRODUCTION

Knowledge of population genetic structure and identification of barriers to gene flow in marine organisms are important to understanding genetic-differentiation and speciation processes in the sea. Apart from obvious barriers such as continents, potential barriers to gene flow in the sea include oceanic fronts, temperature and salinity barriers, oligotrophy (e.g. as a mortality factor in drifting larvae), and predation (Palumbi, 1994; Graves, 1998). Genetic differentiation between populations may also arise from spawning asynchrony among populations, retention of eggs and larvae, and adult homing behaviour (Taylor & Hellberg, 2003). An increasing number of marine taxa with high dispersal potential that were once thought to represent a single species distributed over several oceans (references in Briggs, 1960, 1974) are now recognized as multiple species, thanks to the development of molecular methods (Knowlton, 1993, 2000; Colborn et al., 2001, and references therein).

Pelagic fishes generally exhibit little geographic differentiation across oceans (Theisen et al., 2008 and references therein), although a few exceptions have been reported (Perrin & Borsa, 2001; Rohfritsch & Borsa, 2005; Lu et al., 2006; Sulaiman & Ovenden, 2009). One such case is the narrow-barred Spanish mackerel (Scomberomorus commerson Lacepède, 1800), for which evidence of strong population structure has been reported (Buckworth et al., 2007) and where preliminary phylogeographic investigations have suggested a possible phylogeographic gap coinciding with Wallace's Line (Sulaiman & Ovenden, 2009). Scomberomorus commerson is an inshore pelagic species capable of long migrations (Collette & Russo, 1984). At the basis of important commercial, recreational, and artisanal fisheries, the annual world catch of S. commerson has steadily increased from less than 70 000 tons in the 1970s to over 220 000 tons in 2008 (Fisheries and Aquaculture Information and Statistics Service, FAO; http://www.fao.org/fishery/species/3280/en). Spanish mackerels (genus Scomberomorus) constitute the most speciose group in the family Scombridae (Collette & Russo, 1984). Eighteen species are currently recognized in that genus, the majority of which have a geographic distribution limited to a single ocean basin (Collette & Russo, 1984). The distribution of S. commerson spans more than 160° longitude and 80° latitude in the Indo-West Pacific (Fig. 1), whereas all other Scomberomorus species are distributed within 80° longitude and 80° latitude, and are often restricted to an even smaller range (Collette & Russo, 1984). Therefore, the wide geographic distribution of S. commerson contrasts with that of all other species in the genus Scomberomorus. As species in the genus Scomberomorus share a similar morphology, similar ecological traits, and apparently similar abilities for migration, this contrast in their respective geographic distributions is remarkable. A possible explanation might be that S. commerson actually consists of two or more cryptic species, each with narrower geographic distribution. Significant morphometric differences have been noted between populations across the Indo-Pacific (Collette & Russo, 1984).

Figure 1

Sampling locations of narrow-barred Spanish mackerel, Scomberomorus commerson, across the Indo-West pacific. Pie diagrams represent control-region haplotype frequencies (hatched, clade I; black, haplogroup ii; dark grey, subclade IIa; dotted, subclade IIb; white, subclade IIc; pale grey, subclade IId; see Fig. 2). Abbreviations: BALI, Bali; JAVA, Java Sea; NCAL, New Caledonia; WPAP, West Papua. Samples AUH, BAH, DIB, IRN, KUW, OMN, and RAK were from Hoolihan, Anandh & van Herwerden (2006); samples 1–12 were from Sulaiman & Ovenden (2009). Shaded area: distribution range of S. commerson (Collette & Russo, 1984; Carpenter & Niem, 2001).

Figure 1

Sampling locations of narrow-barred Spanish mackerel, Scomberomorus commerson, across the Indo-West pacific. Pie diagrams represent control-region haplotype frequencies (hatched, clade I; black, haplogroup ii; dark grey, subclade IIa; dotted, subclade IIb; white, subclade IIc; pale grey, subclade IId; see Fig. 2). Abbreviations: BALI, Bali; JAVA, Java Sea; NCAL, New Caledonia; WPAP, West Papua. Samples AUH, BAH, DIB, IRN, KUW, OMN, and RAK were from Hoolihan, Anandh & van Herwerden (2006); samples 1–12 were from Sulaiman & Ovenden (2009). Shaded area: distribution range of S. commerson (Collette & Russo, 1984; Carpenter & Niem, 2001).

Although S. commerson is usually regarded as a highly migratory fish (Collette & Russo, 1984, and references therein), significant allozyme-genetic differences have also been reported between populations at the regional scale. Three main stocks, centred on northern Australia, Papua New Guinea, and Fiji (Shaklee, Phelps & Salini, 1990), and a fourth stock off Queensland (J.B. Shaklee in Buckworth et al., 2007), have been delineated. Based on sequence polymorphism of the mitochondrial control region, Sulaiman & Ovenden (2009) hypothesized an east–west phylogeographic division within the Indo-Malay-Papua archipelago (IMPA). Conversely, no genetic differences were detected at the same locus, among samples from the Persian Gulf and the Oman Sea (Hoolihan, Anandh & van Herwerden, 2006).

So far, no population genetic study of S. commerson has covered a significant part of its distribution and, to our knowledge, no attempt has been made to combine the results from studies that shared the same genetic markers. The objective of the present paper is to investigate the phylogeography of S. commerson at the scale of the Indo-West Pacific to: (1) test the east–west phylogeographical hypothesis of Sulaiman & Ovenden (2009); and (2) examine the possible occurrence of additional distinct populations of S. commerson, from the Persian Gulf to New Caledonia, that is, across over 120° (∼ 71%) of its longitudinal range. For this, we analysed samples from the IMPA (Java Sea, Bali, West Papua) and from New Caledonia using mitochondrial and nuclear genetic markers, and merged the resulting data set with previously published mitochondrial sequence data sets from the Persian Gulf and Oman Sea (Hoolihan et al., 2006) and from the central Indo-West Pacific (Sulaiman & Ovenden, 2009).

MATERIAL AND METHODS

Sampling

Fin clips were sampled from S. commerson obtained from fishermen or from retailers at fish landing places in Indonesia (Java Sea, Bali), West Papua, and New Caledonia (Fig. 1) in 2007–2009. The ‘Java Sea’ sample (JAVA; N = 4) was collected at the Muara Angke fish market, Jakarta; the ‘Bali’ sample (BALI; N = 6), consisting of fish from the Bali Strait, was collected at the Jimbaran fish market, southern Bali; the ‘West Papua’ sample (WPAP) consisted of ten individuals fished in Raja Ampat waters and sold at the Sorong fish market, north-western West Papua; the ‘New Caledonia’ sample (NCAL) consisted of a total of N = 194 individuals fished in the Belep Islands in the northern lagoon of New Caledonia, and off Nepoui (north-western lagoon of New Caledonia), Nouméa (south-western lagoon), and Canala (eastern coast), all obtained from local fishermen. Fin clips were preserved in 95% ethanol and shipped to Perpignan, France, for analysis.

Molecular analysis

Genomic DNA was isolated from fin clips using the Gentra Puregene Tissue Kit (Qiagen), following the manufacturer's protocol. Amplification of the highly variable 5′ end of the mitochondrial control region (380–383 bp) was performed by polymerase chain reaction (PCR) using the universal CR-A (5′-TTCCACCTCTAACTCCCAAAGCTAG-3′) and CR-E (5′-CCTGAAGTAGGAACCAGATG-3′) primers (Lee et al., 1995). Each PCR was performed in 25 µL of reaction mixture containing PCR buffer (Promega), 0.5 mM MgCl2, 0.08 mM of each dNTP, 0.2 µM of each primer, 0.5 U of GoTaq™ DNA Polymerase (Promega), and about 30 ng of genomic DNA. The amplification of the control-region fragment was achieved by 35 cycles of denaturation (30 s at 94 °C), annealing (30 s at 51 °C), and extension (1 min at 72 °C).

A random subsample of individuals from the Java Sea (N = 4), Bali (N = 6), West Papua (N = 7), and a subsample of New Caledonia (N = 74; including individuals of haplogroup ii and randomly chosen individuals representing each of the three subclades IIA, IIb, and IId; see Results) were PCR-amplified for an additional 281-bp fragment of the cytochrome b gene, using primers CB1-L (5′-CCATCCAACATCTCAGCATGATGAAA-3′) and modified CB2-H (5′-CCCTCAGAATGATATTGGTCCTCA-3′) (Palumbi et al., 1991). The reaction mixture and PCR parameters were the same as those used for the control region. PCR products were sent to GATC Biotech for nucleotide sequencing: after purification, the PCR-amplified DNA fragments were sequenced using the CR-A primer (for the control-region fragment) or the CB1-L primer (for the cytochrome b gene fragment), and the sequence reaction products were run on an ABI 3730XL automated sequencer (Applied Biosystems). All nucleotide sequences were deposited in GenBank (http://www.ncbi.nlm.nih.gov) under accession numbers HQ403255–HQ403349 (cytochrome b) and HQ403350–HQ403561 (control region).

We further amplified alleles at eight microsatellite loci specifically developed for S. commerson from the Persian Gulf and the Oman Sea (van Herwerden et al., 2006) in the individuals from Bali, Java, West Papua, and in a subsample of 66 randomly selected individuals from New Caledonia. Forward primers were labelled with ABI fluorescent dyes as follows: C83Sc/6-FAM, L42Sc/6-FAM, H96Sc/VIC, D61Sc/VIC, J43Sc/PET, E27Sc/PET, F6Sc/NED, and J10Sc/NED. All eight microsatellite marker DNAs were amplified in a single 10-µL multiplex PCR reaction using the Type-it Microsatellite PCR Kit (Qiagen), according to the manufacturer's protocol, using an annealing temperature of 57 °C. Amplified fragments were sent to GATC Biotech where they were separated on an ABI 3730XL sequencer, with a GeneScan LIZ-500 internal size standard (Applied Biosystems). GENEMAPPER software (Applied Biosystems) was used to genotype all individuals screened. Finally, GMCONVERT (Faircloth, 2006) was used to convert the GENEMAPPER table of genotypes into a GENEPOP (Raymond & Rousset, 1995) input file.

Data analysis

Nucleotide sequences were aligned visually using BIOEDIT (Hall, 1999). A median-joining parsimony analysis was performed using NETWORK (Bandelt, Forster & Röhl, 1999) on the nucleotide sequence matrix of 454 individual control region haplotypes, aligned over 310 bp, comprising the 216 new sequences produced in this study, aligned with sequences available from the literature. The latter included the Persian Gulf/Oman Sea data set of Hoolihan et al. (2006) (193 sequences; GenBank AM234345–AM234537), the Indo-Malay data set of Sulaiman & Ovenden (2009) (47 sequences re-constructed from their table 1 and from the single GenBank sequence deposited by the authors, GenBank EU526382, identified by us as being that of haplotype ScPHI01), and two sequences from J. R. Ovenden and R. Street (in Buckworth et al., 2007), ScEC10 from eastern Australia (AY205242) and ScNA12 from northern Australia (AY205243), which were added to Sulaiman & Ovenden's (2009) samples ‘12’ and ‘11’, respectively. The root of the network was determined using an out-group (Scomberomorus niphonius; GenBank nos. FJ69105–FJ659112) by maximum parsimony analysis in MEGA 5 (Tamura et al., 2011). The choice of S. niphonius as out-group was motivated by its close systematic proximity with S. commerson (Collette & Russo, 1984) and by the availability of control-region sequences for this species in GenBank.

To assess rates of genetic divergence between groups of lineages, nucleotide substitution models that best fit mitochondrial control-region and cytochrome b sequence data were tested using a model selection analysis based on the maximum-likelihood method implemented in MEGA. Evolutionary models of nucleotide substitution were different relative to mitochondrial DNA fragments. The tests revealed that the model that best fits the mitochondrial control-region data was HKY85 (Hasegawa, Kishino & Yano, 1985), with gamma correction and a heterogeneous proportion of invariable sites. However, because this model was not available in MEGA and ARLEQUIN 3.11 (Excoffier, Laval & Schneider, 2005), for further analysis we chose to use the second-best fit, which was TN93 (Tamura & Nei, 1993), with gamma correction (G = 0.76) and a heterogeneous proportion of invariable sites (I = 0.42). The model that best fit the cytochrome b data was the Kimura two-parameters model (Kimura, 1980). Mean net nucleotide divergences, defined as dxy − 0.5(dx + dy), which subtracts the average ‘within-group’ divergence from the observed ‘between-group’ estimate (Nei & Li, 1979) were then estimated using the appropriate nucleotide substitution model for each sequence set in MEGA. Finally, the Tamura three-parameter model (Tamura, 1992), with a heterogeneous proportion of invariable sites (I = 0.84), was found to be the best fit for the merged (control region + cytochrome b gene) sequence data set, i.e. composite haplotypes obtained by merging partial sequences of the cytochrome b gene and of the control region for samples from Java Sea, Bali, and West Papua, and a subsample (N = 74) from New Caledonia. The homologous haplotype in S. niphonius was obtained by merging the homologous sequences from GeneBank (cytochrome b, DQ497887; control region, FJ659108), and was used as an out-group.

Genetic diversity within regions was estimated as haplotype diversity (Hd; Nei, 1987), nucleotide diversity (π; Nei, 1987), and the average number of nucleotide differences between two sequences (k; Tajima, 1983), using DNASP 5.10 (Librado & Rozas, 2009). Tajima's D-tests (Tajima, 1989) were conducted in DNASP by pooling samples within regions. Regions were defined on a geographical basis as follows: Persian Gulf + Oman Sea (samples AUH, BAH, DIB, IRN, KUW, OMN and RAK in Fig. 1); East China Sea (1); South China Sea + Malacca Strait (2–8); Java Sea + Bali (JAVA, BALI); Timor Sea + Arafura Sea (9–11); West Papua (WPAP); and Coral Sea (12, NCAL). Observed haplotype diversities were compared with the empirical distribution of this index generated using coalescent simulations (H-test; Depaulis & Veuille, 1998) performed in DNASP. The computer simulations were based on the coalescent process for a neutral infinite-sites model, under the assumption of a large constant population size (Hudson, 1990). DNASP generates the empirical distribution of Hd, obtained from 10 000 simulations under the neutral coalescent process, and given the number of segregating sites observed in each sampled population (the model therefore considers that the number of segregating sites is fixed and that mutations are uniformly distributed, at random, along lineages). DNASP thus estimates the probability of obtaining lower or higher values of Hd, computed from the simulation (Hdsim), than the one observed in each sample (Hdobs). These simulations were conducted within regions, pooling samples as described above.

Nucleotide sequence divergences between populations were estimated using Φ statistics and Tamura & Nei's (1993) nucleotide substitution model with gamma correction (G = 0.76) in ARLEQUIN 3.11 (Excoffier et al., 2005). P values were obtained using a non-parametric permutation procedure with 10 000 permutations on the original matrix of sequences. A hierarchical analysis of molecular variance (AMOVA; Excoffier, Smouse & Quattro, 1992) was performed using ARLEQUIN to examine the partitioning of the total variance among regional groups of samples. The significance of Φ statistics and associated variance components was tested by 10 000 random permutations.

For the microsatellite data set, genetic diversity within samples was estimated at the eight loci as the observed (HO) and expected (HE) heterozygosities in GENETIX 4.05 (Belkhir et al., 2004). Deviations from Hardy–Weinberg (HW) equilibrium were estimated within and over all samples using Weir & Cockerham's (1984) inbreeding coefficient, and departures from HW expectations were assessed using the permutation procedure in GENETIX. Null alleles were detected with MICRO-CHECKER (van Oosterhout et al., 2004). Pairwise genetic divergences among samples were estimated using Weir & Cockerham's (1984) multilocus estimator of FST (graphic), and their significance was tested with 10 000 permutations of individuals among samples in GENETIX. Correspondence analysis (Benzécri, 1973) was performed on the matrix of individual multilocus genotypes using GENETIX. The sequential Bonferroni correction (Rice, 1989) was applied for each test.

RESULTS

The length of the amplified mitochondrial DNA control-region fragment in samples JAVA, BALI, WPAP, and NCAL (total N = 212) varied between 380 and 383 nucleotides. The nucleotide sequences were unambiguously aligned over 385 bp. Those 212 sequences were then aligned with 242 control-region sequences available from the literature (see Material and methods), producing a total data set of 454 sequences aligned over 310 bp. We observed 119 variable nucleotide sites: 148 substitutions were counted, 19 of which were singletons. The G + C content was close to 30%. A total of 221 haplotypes were scored: total haplotype diversity was 0.959, nucleotide diversity was 0.088, and the average number of nucleotide differences between two haplotypes was 24.74.

The median-joining parsimony network connecting all 221 control-region haplotypes is presented in Figure 2. Maximum parsimony analysis conducted using S. niphonius sequences as the out-group placed the root of the network between a group comprising all haplotypes sampled in the Persian Gulf and the Oman Sea (hereafter designated as clade I) and the rest of the samples (south-east Asia and Oceania, clade II). The mean net divergence between clades I and II was 14.5 ± 3.0%. clade II comprised four main subclades (hereafter named IIa, IIb, IIc, and IId) radiating from a central, poorly defined group of haplotypes (here coined ‘haplogroup ii’, and delineated by a dashed circle in Fig. 2) with no bootstrap support. Subclade IIa consisted of haplotypes exclusively found in New Caledonia. Subclade IIb comprised haplotypes sampled exclusively in the Coral Sea and in West Papua. Subclade IIc comprised haplotypes sampled exclusively in the East and South China seas and adjacent Java Sea. Subclade IId comprised haplotypes exclusively from northern Australia and the Coral Sea. Haplotypes from the central haplogroup ii were distributed from the South China Sea to New Caledonia, and were dominant in the eastern part of the IMPA. Subclades IIa, IIc, and IId were strongly supported, with bootstrap scores ranging from 81 to 98%. Subclade IIb was poorly supported statistically, presumably because of the low number of mutations that separated it from the central haplogroup ii. Nevertheless, subclade IIb was distinct, as it mostly consisted of haplotypes arranged in a star-like fashion around a dominant haplotype.

Figure 2

Median-joining parsimony network (Bandelt et al., 1999) of control-region haplotypes for the narrow-barred Spanish mackerel Scomberomorus commerson. Groups of haplotypes delineated according to genetic proximity, with an indication of area of occurrence: two main clades, numbered I and II, the latter with subclades IIa, IIb, IIc, and IId, radiating from a central haplogroup ii. Branch lengths are proportional to the number of mutational steps; closed circles represent individual haplotypes, with their area being proportional to their frequency in the total sample; arrow, indicates root. Bootstrap support (neighbour-joining algorithm; Kimura two-parameter distances; 1000 resampling runs; Tamura et al., 2007): 100% for clade I; 98% for subclade IIa; 32% for subclade IIb; 81% for subclade IIc; 98% for subclade IId. Scale bar: five mutational steps.

Figure 2

Median-joining parsimony network (Bandelt et al., 1999) of control-region haplotypes for the narrow-barred Spanish mackerel Scomberomorus commerson. Groups of haplotypes delineated according to genetic proximity, with an indication of area of occurrence: two main clades, numbered I and II, the latter with subclades IIa, IIb, IIc, and IId, radiating from a central haplogroup ii. Branch lengths are proportional to the number of mutational steps; closed circles represent individual haplotypes, with their area being proportional to their frequency in the total sample; arrow, indicates root. Bootstrap support (neighbour-joining algorithm; Kimura two-parameter distances; 1000 resampling runs; Tamura et al., 2007): 100% for clade I; 98% for subclade IIa; 32% for subclade IIb; 81% for subclade IIc; 98% for subclade IId. Scale bar: five mutational steps.

Expanding the length of the mitochondrial fragment on a subsample of individuals allowed us to resolve the phylogenetic placement of haplotypes included in the above, undefined haplogroup ii (Fig. 3). Clade-II haplotypes thus clustered into four distinct subclades: namely IIa (IIb + ii, where haplogroup ii appeared as a sister branch to IIb), IIc, and IId (Fig. 3). Mean net divergence between subclades, estimated from control-region nucleotide sequences, varied from 9.0 ± 2.3% (between subclades IIa and IId) to 11.8 ± 2.8% (between subclades IIa and IIc). Mean net divergence between subclades estimated from partial cytochrome b gene sequences alone ranged from 0.3 ± 0.3% (between subclades IIa and IId) to 2.5 ± 0.9% [between subclades (IIb + ii) and IIc].

Figure 3

Neighbour-joining tree (Tamura three-parameter distances; MEGA 5) of composite mitochondrial sequences (281 bp of cytochrome b gene + 392 bp of control region) from the narrow-barred Spanish mackerel Scomberomorus commerson. The arrow indicates the root.

Figure 3

Neighbour-joining tree (Tamura three-parameter distances; MEGA 5) of composite mitochondrial sequences (281 bp of cytochrome b gene + 392 bp of control region) from the narrow-barred Spanish mackerel Scomberomorus commerson. The arrow indicates the root.

Genetic divergence estimates among sampling locations, expressed as pairwise ΦST, ranged from 0 to 0.965 (Table 1). However, some pairwise ΦST estimates should be taken with caution because of low sample sizes, e.g. between sample 2 from Thailand and sample 9 from Kupang, West Timor (ΦST = 0.965), generally high levels of genetic differentiation were observed between samples (Table 1). The Persian Gulf/Oman Sea samples were very distinct from all other samples (ΦST = 0.735–0.800). Within the latter group, the West Papuan and New Caledonian samples were themselves much differentiated from all the other samples (ΦST = 0.251–0.851). Pairwise ΦST estimates were also generally high within the Coral Triangle, although not significant, presumably because of low sample sizes. The AMOVA indicated that a large and highly significant proportion (75%) of the total mitochondrial variance resided among regions (Table 2). Excluding the East China Sea sample (because of its low size) and the Indian Ocean samples, the proportion of the total mitochondrial variance attributed among five geographically defined groups of samples across the IMPA and south-western Pacific Ocean [(South China Sea + Malacca Strait); (Java Sea + Bali); (Timor Sea + Arafura Sea); West Papua; Coral Sea)] was high (46%, P « 0.001). A small but significant part of the total variance (1.23%) was observed within groups, whereas 26% of the total variance was attributed among individuals within samples (Table 2). Within geographically defined regions, estimated haplotype diversity ranged from 0.82 in the Coral Sea to 1 in Java Sea/Bali, and nucleotide diversity ranged from 0.023 in West Papua to 0.017 in the East China Sea (Table 3). The mean number of nucleotide differences between two sequences ranged from 6.72 in West Papua to 15.26 in the South China Sea/Malacca Strait (Table 3). Most samples (here grouped by region) showed higher haplotype diversity than expected from the neutral coalescent theory on the basis of the number of observed segregating sites [Depaulis & Veuille's, (1998) haplotype diversity test; Table 3], whereas none of them deviated from neutrality according to Tajima's D-test (Table 3). Low sample sizes for some samples, or non-equilibrium conditions, may explain this discrepancy.

Table 1

Pairwise ΦST estimates based on sequences of the 5′ end mitochondrial control region among narrow-barred Spanish mackerel (Scomberomorus commerson) samples (above diagonal)

Sample (NPG + OS JAVA BALI 10 11 12 WPAP NCAL 
PG + OS (193) – 0.772 0.770 0.784 0.746 0.735 0.763 0.744 0.780 0.788 0.747 0.800 0.773 0.749 0.785 0.787 0.765 
 1 (2) *** – 0.002 0.091 −0.166 0.081 −0.059 0.470 −0.077 0.352 0.213 0.932 0.684 0.692 0.645 0.829 0.670 
 2 (2) *** NS – 0.326 −0.102 0.133 0.071 0.526 0.009 0.433 0.275 0.965 0.715 0.729 0.664 0.851 0.657 
 3 (2) *** NS NS – −0.197 0.064 0.051 0.438 −0.215 0.299 0.241 0.895 0.667 0.678 0.623 0.811 0.673 
 4 (4) *** NS NS NS – −0.092 −0.119 0.197 −0.124 0.275 0.139 0.651 0.481 0.449 0.493 0.657 0.573 
 5 (5) *** NS NS NS NS – −0.055 −0.033 0.118 0.343 0.144 0.466 0.309 0.195 0.335 0.477 0.421 
 6 (5) *** NS NS NS NS NS – 0.139 0.056 0.327 0.149 0.510 0.451 0.361 0.485 0.567 0.569 
 7 (4) *** NS NS NS NS NS NS – 0.473 0.590 0.307 0.332 0.264 −0.077 0.355 0.376 0.332 
 8 (3) *** NS NS NS NS NS NS NS – 0.314 0.268 0.865 0.669 0.674 0.655 0.804 0.675 
JAVA (4) *** NS NS NS – 0.028 0.860 0.735 0.713 0.712 0.764 0.668 
BALI (6) *** NS NS NS NS NS NS NS NS NS – 0.587 0.517 0.436 0.546 0.463 0.518 
 9 (5) *** ** ** ** – 0.629 0.432 0.626 0.533 0.465 
10 (5) *** ** ** ** ** – 0.295 0.308 0.585 0.367 
11 (5) *** NS NS ** ** – 0.424 0.332 0.251 
12 (5) *** ** ** ** ** ** ** ** – 0.609 0.375 
WPAP (8) *** ** *** *** ** ** ** *** *** *** *** – 0.282 
NCAL (194) *** *** *** *** *** *** *** ** *** *** *** *** ** ** *** ** – 
Sample (NPG + OS JAVA BALI 10 11 12 WPAP NCAL 
PG + OS (193) – 0.772 0.770 0.784 0.746 0.735 0.763 0.744 0.780 0.788 0.747 0.800 0.773 0.749 0.785 0.787 0.765 
 1 (2) *** – 0.002 0.091 −0.166 0.081 −0.059 0.470 −0.077 0.352 0.213 0.932 0.684 0.692 0.645 0.829 0.670 
 2 (2) *** NS – 0.326 −0.102 0.133 0.071 0.526 0.009 0.433 0.275 0.965 0.715 0.729 0.664 0.851 0.657 
 3 (2) *** NS NS – −0.197 0.064 0.051 0.438 −0.215 0.299 0.241 0.895 0.667 0.678 0.623 0.811 0.673 
 4 (4) *** NS NS NS – −0.092 −0.119 0.197 −0.124 0.275 0.139 0.651 0.481 0.449 0.493 0.657 0.573 
 5 (5) *** NS NS NS NS – −0.055 −0.033 0.118 0.343 0.144 0.466 0.309 0.195 0.335 0.477 0.421 
 6 (5) *** NS NS NS NS NS – 0.139 0.056 0.327 0.149 0.510 0.451 0.361 0.485 0.567 0.569 
 7 (4) *** NS NS NS NS NS NS – 0.473 0.590 0.307 0.332 0.264 −0.077 0.355 0.376 0.332 
 8 (3) *** NS NS NS NS NS NS NS – 0.314 0.268 0.865 0.669 0.674 0.655 0.804 0.675 
JAVA (4) *** NS NS NS – 0.028 0.860 0.735 0.713 0.712 0.764 0.668 
BALI (6) *** NS NS NS NS NS NS NS NS NS – 0.587 0.517 0.436 0.546 0.463 0.518 
 9 (5) *** ** ** ** – 0.629 0.432 0.626 0.533 0.465 
10 (5) *** ** ** ** ** – 0.295 0.308 0.585 0.367 
11 (5) *** NS NS ** ** – 0.424 0.332 0.251 
12 (5) *** ** ** ** ** ** ** ** – 0.609 0.375 
WPAP (8) *** ** *** *** ** ** ** *** *** *** *** – 0.282 
NCAL (194) *** *** *** *** *** *** *** ** *** *** *** *** ** ** *** ** – 

PG + OS, ‘Persian Gulf + Oman Sea’, comprising samples AUH, BAH, DIB, IRN, KUW, OMN, and RAK of Hoolihan et al. (2006); samples 1–12, Sulaiman & Ovenden (2009); others, see Figure 1. N, number of sequences (in brackets). Significance levels (based on 10 000 permutations of haplotypes among samples) are indicated below the diagonal: NS, non significant; *P < 0.05; **P < 0.010; ***P < 0.001.

Above diagonal, bold: values remaining significant after sequential Bonferroni correction (Rice, 1989).

Table 2

Analysis of molecular variance (AMOVA) among samples of Scomberomorus commerson based on partial sequences (310 bp) of the mitochondrial control region

Source of variation d.f. Sum of squares Variance components % variation P 
Among groups 5787.59 Va = 20.49 72.79 < 0.0001 
Among populations within groups 20 266.97 Vb = 0.35 1.23 0.0014 
Within groups 424 3100.91 Vc = 7.31 25.98 < 0.0001 
Total 450 9155.48 28.15   
Source of variation d.f. Sum of squares Variance components % variation P 
Among groups 5787.59 Va = 20.49 72.79 < 0.0001 
Among populations within groups 20 266.97 Vb = 0.35 1.23 0.0014 
Within groups 424 3100.91 Vc = 7.31 25.98 < 0.0001 
Total 450 9155.48 28.15   

Samples were grouped on a geographic basis (see Fig. 1) as follows: Persian Gulf + Oman Sea (samples AUH, BAH, DIB, IRN, KUW, OMN, and RAK); East China Sea (1); South China Sea + Malacca Strait (2–8); Java Sea + Bali (JAVA, BALI); Timor Sea + Arafura Sea (9–11); West Papua (WPAP); Coral Sea (12, NCAL).

Table 3

Genetic diversity and results of neutrality tests in populations of Scomberomorus commerson across the Indo-Pacific

Region N H S Hd π k H-test D 
Persian Gulf/Oman Sea 193 123 79 0.986 0.040 11.74 0.999 −0.407 
East China Sea 1.000 0.017 5.00 – – 
South China Sea/Malacca Strait 25 20 55 0.980 0.053 15.26 0.974 0.185 
Java Sea/Bali 10 10 42 1.000 0.052 15.18 0.999 0.109 
Timor Sea/Arafura Sea 16 15 39 0.992 0.038 11.36 0.999 −0.041 
West Papua 17 0.929 0.023 6.72 0.726 0.124 
Coral Sea 200 62 66 0.817 0.041 11.94 0.011 0.191 
Region N H S Hd π k H-test D 
Persian Gulf/Oman Sea 193 123 79 0.986 0.040 11.74 0.999 −0.407 
East China Sea 1.000 0.017 5.00 – – 
South China Sea/Malacca Strait 25 20 55 0.980 0.053 15.26 0.974 0.185 
Java Sea/Bali 10 10 42 1.000 0.052 15.18 0.999 0.109 
Timor Sea/Arafura Sea 16 15 39 0.992 0.038 11.36 0.999 −0.041 
West Papua 17 0.929 0.023 6.72 0.726 0.124 
Coral Sea 200 62 66 0.817 0.041 11.94 0.011 0.191 

N, number of individuals; H, number of haplotypes; Hd, haplotype diversity (Nei, 1987); S, number of polymorphic sites; π, nucleotide diversity (Nei, 1987); and k, average number of nucleotide differences between two sequences (Tajima, 1983). For the H-test (Depaulis & Veuille, 1998), the probability that Hdsim< Hdobs is reported. The value of Tajima's D-test (Tajima, 1989) is reported. Samples were grouped as in Table 2.

Genetic diversity estimated from the analysis of the eight microsatellite loci is presented in Table 4. The average observed heterozygosity across all eight loci ranged from 0.521 in Bali to 0.571 in New Caledonia. The distribution of genotype frequencies at a locus did not differ significantly from HW expectations (standard Bonferroni correction applied) except for sample NCAL at locus D61Sc (Weir & Cockerham's graphic = 0.639; P < 0.001). Null alleles were detected at loci D61Sc in New Caledonia, L42Sc in Bali, and E27Sc in West Papua. The graphical illustration of the correspondence analysis conducted on microsatellite multilocus genotypes revealed geographic partition: individuals from West Papua and New Caledonia formed two completely disjunct clusters, themselves clearly separated from an ensemble including all individuals sampled in Bali and in the Java Sea (Fig. 4). So, there was no evidence of interchange of individuals among subregions in the south-western tropical Pacific. Estimates of Weir & Cockerham's parameter of genetic differentiation (θ) were graphic = 0.233 between Java/Bali and West Papua, graphic = 0.313 between Java/Bali and New Caledonia, and graphic = 0.202 between West Papua and New Caledonia.

Table 4

Genetic diversity at eight microsatellite loci in Scomberomorus commerson samples (sample size in brackets)

Locus, parameter  Sample 
NCAL  BALI  JAVA  WPAP 
(N = 66)  (N = 6)  (N = 4)  (N = 10) 
C83Sc         
H E  0.488  0.583  0.531  0.725 
H O  0.515  0.667  0.500  1.000 
graphic  −0.049  −0.053  0.200  −0.333 
D61Sc         
H E  0.342  0.486  0.563  0.500 
H O  0.125  0.167  0.750  0.143 
graphic  0.639*  0.706  −0.200  0.750 
E27Sc         
H E  0.498  0.153  0.000  0.580 
H O  0.500  0.167  0.000  0.200 
graphic  0.003  0.000  –  0.684 
F6Sc         
H E  0.499  0.375  0.219  0.185 
H O  0.585  0.500  0.250  0.200 
graphic  −0.164  −0.250  0.000  −0.029 
H96Sc         
H E  0.618  0.694  0.688  0.595 
H O  0.667  1.000  1.000  0.700 
graphic  −0.071  −0.364  −0.333  −0.125 
J10Sc         
H E  0.510  0.569  0.219  0.505 
H O  0.561  0.667  0.250  0.500 
graphic  −0.091  −0.081  0.000  0.063 
J43Sc         
H E  0.565  0.667  0.688  0.560 
H O  0.710  0.500  0.750  0.800 
graphic  −0.249  0.333  0.053  −0.385 
L42Sc         
H E  0.884  0.847  0.781  0.895 
H O  0.906  0.500  1.000  0.900 
graphic  −0.017  0.483  −0.143  0.047 
Multilocus         
H E  0.550  0.547  0.461  0.568 
H O  0.571  0.521  0.563  0.555 
graphic  −0.030  0.138  −0.080  0.080 
Locus, parameter  Sample 
NCAL  BALI  JAVA  WPAP 
(N = 66)  (N = 6)  (N = 4)  (N = 10) 
C83Sc         
H E  0.488  0.583  0.531  0.725 
H O  0.515  0.667  0.500  1.000 
graphic  −0.049  −0.053  0.200  −0.333 
D61Sc         
H E  0.342  0.486  0.563  0.500 
H O  0.125  0.167  0.750  0.143 
graphic  0.639*  0.706  −0.200  0.750 
E27Sc         
H E  0.498  0.153  0.000  0.580 
H O  0.500  0.167  0.000  0.200 
graphic  0.003  0.000  –  0.684 
F6Sc         
H E  0.499  0.375  0.219  0.185 
H O  0.585  0.500  0.250  0.200 
graphic  −0.164  −0.250  0.000  −0.029 
H96Sc         
H E  0.618  0.694  0.688  0.595 
H O  0.667  1.000  1.000  0.700 
graphic  −0.071  −0.364  −0.333  −0.125 
J10Sc         
H E  0.510  0.569  0.219  0.505 
H O  0.561  0.667  0.250  0.500 
graphic  −0.091  −0.081  0.000  0.063 
J43Sc         
H E  0.565  0.667  0.688  0.560 
H O  0.710  0.500  0.750  0.800 
graphic  −0.249  0.333  0.053  −0.385 
L42Sc         
H E  0.884  0.847  0.781  0.895 
H O  0.906  0.500  1.000  0.900 
graphic  −0.017  0.483  −0.143  0.047 
Multilocus         
H E  0.550  0.547  0.461  0.568 
H O  0.571  0.521  0.563  0.555 
graphic  −0.030  0.138  −0.080  0.080 

H E, expected heterozygosity according to Hardy–Weinberg equilibrium; HO, observed heterozygosity; graphic, Weir & Cockerham's (1984) estimate fixation index.

*

Significant deviation after standard Bonferroni correction (Rice, 1989).

Figure 4

Correspondence analysis: projection on the two dimensions defined by axes 1 and 3, of 86 individuals of the Narrow-barred Spanish mackerel, Scomberomorus commerson, sampled from Bali (BALI, N = 6), Java Sea (JAVA, N = 4), West Papua (WPAP, N = 10), and New Caledonia (NCAL, N = 66), characterized by their genotype at eight microsatellite loci. Multiple nuclear locus estimates of genetic differentiation were as follows: JAVA vs. BALI, graphic= 0.014, P = 0.369; (JAVA + BALI) vs. WPAP, graphic = 0.231, P < 0.001; (JAVA + BALI) vs. NCAL, graphic = 0.311, P < 0.001; WPAP vs. NCAL, graphic = 0.202, P < 0.001.

Figure 4

Correspondence analysis: projection on the two dimensions defined by axes 1 and 3, of 86 individuals of the Narrow-barred Spanish mackerel, Scomberomorus commerson, sampled from Bali (BALI, N = 6), Java Sea (JAVA, N = 4), West Papua (WPAP, N = 10), and New Caledonia (NCAL, N = 66), characterized by their genotype at eight microsatellite loci. Multiple nuclear locus estimates of genetic differentiation were as follows: JAVA vs. BALI, graphic= 0.014, P = 0.369; (JAVA + BALI) vs. WPAP, graphic = 0.231, P < 0.001; (JAVA + BALI) vs. NCAL, graphic = 0.311, P < 0.001; WPAP vs. NCAL, graphic = 0.202, P < 0.001.

DISCUSSION

Phylogeographic breaks between Indian and western Pacific populations have been reported for several widely distributed Indo-Pacific species (Benzie, 1999; Barber, Erdmann & Palumbi, 2006; Kochzius & Nuryanto, 2008; Carpenter et al., 2011). Barriers to gene flow have been located within the IMPA, although the precise location varies extensively across species (Carpenter et al., 2011). In fishes, phylogeographic studies spanning the IMPA have shown evidence of subdivision between Indian Ocean versus Pacific Ocean populations in a number of benthic shore species (e.g. Lourie, Green & Vincent, 2005; Drew & Barber, 2009; Carpenter et al., 2011), but not in others (e.g. Stepien, Randall & Rosenblatt, 1994; Klanten, van Herwerden & Choat, 2007; Horne et al., 2008; Gaither et al., 2010; Winters et al., 2010). As, unlike most benthic fishes, pelagic fishes add extreme adult mobility to passive dispersal at the larval and juvenile stages, it is appropriate to consider them separately: genetic studies of large pelagic fishes such as tunas, wahoo, and swordfish have not yielded conclusive evidence of geographic partition across the Indo-Pacific (Alvarado Bremer et al., 1998; Chow & Takeyama, 2000; Durand et al., 2005; Ely et al., 2005; Gonzalez, Beerli & Zardoya, 2008; Theisen et al., 2008; Diaz Jaimes et al., 2010), whereas contrasted patterns have been found in small inshore pelagic fishes like scad mackerels (genus Decapterus; Carangidae), from broadscale geographic homogeneity (Decapterus macrosoma; Borsa, 2003) to sharp population partition (Decapterus russelli; Rohfritsch & Borsa, 2005) within the IMPA. A summary of genetic- differentiation index estimates in broadcast-spawning Indo-West Pacific bony fishes of various habitats is presented in Table 5. The level of geographic differentiation in S. commerson is much higher than all other pelagic species studied so far, with the possible exception of D. russelli, and it is matched only by rare examples of benthic shore species (Table 5).

Table 5

Estimates of population genetic (mtDNA) differentiation (Fst or equivalent) reported for broadcast-spawning Indo-Pacific bony fishes of various habitats

Habitat, species Geographic range considered Marker locus Fst Reference 
Reef-associated     
Parrotfish, Chlorurus sordidus Western Pacific + northern Australia† CR, sequence 0.031 Bay et al. (2003
Bigscale soldierfish, Myripristis berndti Indo-Pacific cyt b, sequence 0.580 Craig et al. (2007
Bignose unicornfish, Naso vlamingii Western Pacific‡ CR, sequence 0.079 Klanten, van Herwerden & Choat (2007
Forktail rabbitfish, Siganus argenteus Western Pacific cyt b, sequence 0.031 Lemer et al. (2007) 
Lined unicornfish, Naso brevirostris Indo-Pacific CR, sequence 0.000–0.163 Horne et al. (2008
Bluespine unicornfish, Naso unicornis Indo-Pacific CR, sequence 0.000–0.033 Horne et al. (2008
Coral trout, Plectropomus leopardus Western Pacific + north-western Australia§ CR, sequence 0.892 van Herwerden et al. (2009
Blacktail snapper, Lutjanus fulvus Indo-Pacific cyt b, sequence 0.640 (0.110*) Gaither et al. (2010
Bluestripe snapper, Lutjanus kasmira Indo-Pacific cyt b, sequence 0.000–0.634 (0.062*) Gaither et al. (2010
Demersal     
Crimson snapper, Lutjanus erythropterus Western Pacific CR, sequence 0.086 Zhang et al. (2006) 
Golden snapper, Pristipomoides multidens Indo-Malay region CR, RFLP 0.055 Ovenden et al. (2004
Balloon alfonsino, Beryx mollis Western Pacific cyt b, sequence 0.000 Akimoto et al. (2006
Slender alfonsino, Beryx splendens Western Pacific cyt b, sequence 0.003 Akimoto et al. (2006
Pelagic     
Round scad mackerel, Decapterus macrosoma Indo-Malay region cyt b, SSCP 0.000 Borsa (2003
Bigeye tuna, Thunnus obesus Indo-Pacific RFLP 0.000 Durand et al. (2005
Indian scad mackerel, Decapterus russelli Indo-Malay region¶ cyt b, SSCP 0.370 Rohfritsch & Borsa (2005
Bigeye tuna, Thunnus obesus Western Pacific CR, sequence 0.004 Chiang et al. (2006
Swordfish, Xiphias gladius Indian O. vs. western Pacific CR, sequence 0.038 Lu et al. (2006
Wahoo, Acanthocybium solandri Indo-Pacific cyt b, sequence 0.000–0.054 Theisen et al. (2008
Dolphinfish, Coryphaena hippurus Indo-Pacific NAD1, sequence 0.000–0.016 Diaz Jaimes et al. (2010
Narrow-barred Spanish mackerel, Scomberomorus commerson Indo-Pacific CR, sequence 0.699–0.743 Present study 
Habitat, species Geographic range considered Marker locus Fst Reference 
Reef-associated     
Parrotfish, Chlorurus sordidus Western Pacific + northern Australia† CR, sequence 0.031 Bay et al. (2003
Bigscale soldierfish, Myripristis berndti Indo-Pacific cyt b, sequence 0.580 Craig et al. (2007
Bignose unicornfish, Naso vlamingii Western Pacific‡ CR, sequence 0.079 Klanten, van Herwerden & Choat (2007
Forktail rabbitfish, Siganus argenteus Western Pacific cyt b, sequence 0.031 Lemer et al. (2007) 
Lined unicornfish, Naso brevirostris Indo-Pacific CR, sequence 0.000–0.163 Horne et al. (2008
Bluespine unicornfish, Naso unicornis Indo-Pacific CR, sequence 0.000–0.033 Horne et al. (2008
Coral trout, Plectropomus leopardus Western Pacific + north-western Australia§ CR, sequence 0.892 van Herwerden et al. (2009
Blacktail snapper, Lutjanus fulvus Indo-Pacific cyt b, sequence 0.640 (0.110*) Gaither et al. (2010
Bluestripe snapper, Lutjanus kasmira Indo-Pacific cyt b, sequence 0.000–0.634 (0.062*) Gaither et al. (2010
Demersal     
Crimson snapper, Lutjanus erythropterus Western Pacific CR, sequence 0.086 Zhang et al. (2006) 
Golden snapper, Pristipomoides multidens Indo-Malay region CR, RFLP 0.055 Ovenden et al. (2004
Balloon alfonsino, Beryx mollis Western Pacific cyt b, sequence 0.000 Akimoto et al. (2006
Slender alfonsino, Beryx splendens Western Pacific cyt b, sequence 0.003 Akimoto et al. (2006
Pelagic     
Round scad mackerel, Decapterus macrosoma Indo-Malay region cyt b, SSCP 0.000 Borsa (2003
Bigeye tuna, Thunnus obesus Indo-Pacific RFLP 0.000 Durand et al. (2005
Indian scad mackerel, Decapterus russelli Indo-Malay region¶ cyt b, SSCP 0.370 Rohfritsch & Borsa (2005
Bigeye tuna, Thunnus obesus Western Pacific CR, sequence 0.004 Chiang et al. (2006
Swordfish, Xiphias gladius Indian O. vs. western Pacific CR, sequence 0.038 Lu et al. (2006
Wahoo, Acanthocybium solandri Indo-Pacific cyt b, sequence 0.000–0.054 Theisen et al. (2008
Dolphinfish, Coryphaena hippurus Indo-Pacific NAD1, sequence 0.000–0.016 Diaz Jaimes et al. (2010
Narrow-barred Spanish mackerel, Scomberomorus commerson Indo-Pacific CR, sequence 0.699–0.743 Present study 
*

Marquises population excluded.

Includes samples from Rota (Micronesia), Papua New Guinea, Great Barrier Reef and Western Australia.

Includes Philippines, Papua-New Guinea, eastern Australia and Hawai'i.

§

Includes Taiwan.

Including West Papua.

CR, control region; cyt b, cytochrome b locus; RFLP, restriction fragment length polymorphism of a PCR-amplified fragment of mtDNA.

Distant mitochondrial clades I and II indicate cryptic species

Here, deep phylogeographic partition was observed between the S. commerson population sampled in the Persian Gulf and the Oman Sea, and populations sampled in the IMPA and in the western Pacific. The 14.5% control-region sequence divergence observed between clades I and II, and its correlation with geography, indicates a clear separation between a north-western Indian Ocean (Persian Gulf/Oman Sea) form of S. commerson and a western Pacific Ocean form. This level of nucleotide divergence is higher than the interspecific sequence divergences estimated using the same genetic marker between blue and chub mackerels (Scomber australasicus and Scomber japonicus, Scombridae; 1.7–9.6%; Catanese, Manchado & Infante, 2010), among scad mackerels (Decapterus macarellus, Carangidae; 2.7–11.7%; Arnaud, Bonhomme & Borsa, 1999), and among horse mackerels (Trachurus trachurus, Carangidae; 1.0–9.6%; Cárdenas et al., 2005). It is also higher than interspecific genetic divergences for benthic species, e.g. damselfishes (10.1–12.8%; Bernardi et al., 2002; Timm, Figiel & Kochzius, 2008), and is generally higher than genetic divergence at the interspecific level in fishes (McCune & Lovejoy, 1998; Lessios, 2008).

The present results suggest that the S. commerson form sampled in the IMPA and in the western Pacific Ocean is a species different from that sampled in the Persian Gulf and the Oman Sea. The relatively large differences in morphology and in meristic characters reported between S. commerson sampled in the Indian Ocean and those from the ‘East Indies’ (Collette & Russo, 1984: table 15), are consistent with this hypothesis. Therefore, the large distribution of S. commerson, an exception among species of that genus, might well be an artefact that results from the taxonomic confusion of cryptic species.

Phylogeographic break within the IMPA and further partitioning at the regional scale

Further phylogeographic partitioning was found within the western Pacific Ocean form of S. commerson. Clade-II haplotypes, which characterize that form, clustered into four distinct subclades separated by 0.3–2.5% net nucleotide divergence at the cytochrome b locus, and by 9.0–11.8% for the control region. Subclade IIc was exclusively found, and dominant, in the western half of the IMPA (i.e. west to a line running from Timor to West Papua), whereas subclades IIa and IId were exclusively found east and south of a line running from Bali to the Philippines. The root of the cytochrome b tree presented in Figure 3 was between subclade IIc and the rest, suggesting that the genetic differences between S. commerson populations of the western half of the IMPA and those east and south of it result from vicariance. Subclade IIa was sampled in New Caledonia only, which suggests that it may be endemic to New Caledonia, or at least geographically restricted to the south-eastern extremity of the range of S. commerson. The remaining subclade (IIb + ii) itself consisted of two sister branches (hereafter branch IIb and haplogroup ii), with different geographic distributions. Branch IIb was dominant in New Caledonia, and it was also found in West Papua and in northern Australia. In contrast, haplogroup ii haplotypes were found throughout the IMPA and the western Pacific Ocean.

The distinctness of S. commerson populations from New Caledonia and West Papua was further demonstrated by the strong pairwise divergence estimate over eight microsatellite loci. We are aware of a few genetic studies in other species of the genus Scomberomorus that can be used as a basis of comparison with the present results in S. commerson at the regional scale (Table 6). The observed FST estimates between populations at the regional scale in S. commerson were considerably higher (one order of magnitude for nuclear loci) than those estimated for any other Scomberomorus species. This indicated that very little, if any, gene flow connects S. commerson populations in the tropical south-western Pacific Ocean. Interestingly, FST estimates at the same microsatellite loci within S. commerson sampled in the Persian Gulf and in the Oman Sea were relatively low (range 0.000–0.078), although they were sampled over geographic distances similar to that between Bali and West Papua (van Herwerden et al., 2006).

Table 6

Scomberomorus species. Reported estimates of genetic differentiation (FST or equivalent) at the regional scale

Species Geographic range considered Marker type FST Reference 
  Mitochondrial   
S. cavalla Western Atlantic and Gulf of Mexico Whole mtDNA, RFLP 0.000 Gold, Kristmundsdóttir & Richardson (1997
S. commerson Persian Gulf–Oman Sea CR, RFLP 0.010 Hoolihan et al. (2006
S. commerson Tropical south-western Pacific CR, sequence 0.306–0.586 present study 
S. maculatus Western Atlantic and Gulf of Mexico ND4, RFLP 0.000–0.065 Buonaccorsi, Starkey & Graves (2001
S. niphonius East China Sea–Yellow Sea CR, sequence 0.000–0.047 Shui et al. (2009
S. sierra Pacific coast of Tropical America CR, sequence 0.004–0.094 Domínguez López, Uribe Alcocer & Díaz Jaimes (2010
  Nuclear   
S. brasiliensis Southern Caribbean Sea 8 microsatellite loci* 0.002 Gold et al. (2010
S. cavalla Western Atlantic and Gulf of Mexico 5 microsatellite loci* 0.000–0.013 Broughton, Stewart & Gold (2002
S. cavalla Around peninsular Florida 5 microsatellite loci* 0.000–0.005 Gold, Pak & DeVries (2002
S. commerson Persian Gulf–Oman Sea 5 microsatellite loci* 0.029 van Herwerden et al. (2006
S. commerson Tropical Australia–Papua New Guinea 9 allozyme loci* 0.002 J.B. Shaklee in Buckworth et al. (2007
S. commerson Tropical south-western Pacific 8 microsatellite loci* 0.228 present study 
S. maculatus Western Atlantic and Gulf of Mexico 1 intron locus* 0.000 Buonaccorsi et al. (2001
S. munroi Northern and Eastern Australia 7 allozyme loci* 0.038 Begg et al. (1998
S. queenslandicus Northern and Eastern Australia 7 allozyme loci* 0.025 Begg et al. (1998
Species Geographic range considered Marker type FST Reference 
  Mitochondrial   
S. cavalla Western Atlantic and Gulf of Mexico Whole mtDNA, RFLP 0.000 Gold, Kristmundsdóttir & Richardson (1997
S. commerson Persian Gulf–Oman Sea CR, RFLP 0.010 Hoolihan et al. (2006
S. commerson Tropical south-western Pacific CR, sequence 0.306–0.586 present study 
S. maculatus Western Atlantic and Gulf of Mexico ND4, RFLP 0.000–0.065 Buonaccorsi, Starkey & Graves (2001
S. niphonius East China Sea–Yellow Sea CR, sequence 0.000–0.047 Shui et al. (2009
S. sierra Pacific coast of Tropical America CR, sequence 0.004–0.094 Domínguez López, Uribe Alcocer & Díaz Jaimes (2010
  Nuclear   
S. brasiliensis Southern Caribbean Sea 8 microsatellite loci* 0.002 Gold et al. (2010
S. cavalla Western Atlantic and Gulf of Mexico 5 microsatellite loci* 0.000–0.013 Broughton, Stewart & Gold (2002
S. cavalla Around peninsular Florida 5 microsatellite loci* 0.000–0.005 Gold, Pak & DeVries (2002
S. commerson Persian Gulf–Oman Sea 5 microsatellite loci* 0.029 van Herwerden et al. (2006
S. commerson Tropical Australia–Papua New Guinea 9 allozyme loci* 0.002 J.B. Shaklee in Buckworth et al. (2007
S. commerson Tropical south-western Pacific 8 microsatellite loci* 0.228 present study 
S. maculatus Western Atlantic and Gulf of Mexico 1 intron locus* 0.000 Buonaccorsi et al. (2001
S. munroi Northern and Eastern Australia 7 allozyme loci* 0.038 Begg et al. (1998
S. queenslandicus Northern and Eastern Australia 7 allozyme loci* 0.025 Begg et al. (1998

Scomberomorus commerson appeared as an outlier for both mitochondrial and nuclear markers [Dixon's test for detecting outliers (Sokal & Rohlf, 1969): Q = 0.693, P < 0.05; and Q = 0.833, P < 0.01, respectively].

*

Only polymorphic loci (where estimated allele frequencies < 0.95 in at least one population) were considered in this count.

The clover-like structure of clade II and its relationship to the geographic distribution of haplotypes imply that western Pacific S. commerson populations have been isolated from each other for a period of time long enough to achieve reciprocal monophyly. The geographic distribution of haplotypes correlates with their phylogenetic structure, with the exception of haplogroup-ii haplotypes. A possible explanation for this intriguing pattern is that a proto-population of haplogroup ii, initially restricted to the eastern part of the distribution of S. commerson, secondarily diffused westward to colonize the entire IMPA, where it entered into secondary contact with populations harbouring subclade-IIc haplotypes. The question that follows is whether those two branches are reproductively isolated from each other.

Testing for reproductive isolation

It is possible to test for reproductive isolation between groups of individuals that harbour different mitochondrial types by using nuclear markers, when those groups also happen to occur in sympatry. Microsatellite genotypes were available for three S. commerson samples (BALI, WPAP, and NCAL) with a heterogeneous mitochondrial composition. Heterozygote deficiency was effectively observed at one locus in the NCAL sample, but this was ascribed to null alleles. Therefore, there was no conclusive evidence from microsatellites that the groups of individuals characterized by different clade-II mitochondrial lineages at a given location belong to separate gene pools; hence, one cannot reject the hypothesis that they belong to the same single species, but larger sample sizes of both individuals and loci are needed to further explore the question of reproductive isolation in western Pacific S. commerson.

It is generally hypothesized that highly divergent intraspecific lineages originate either from a recent admixture of formerly isolated populations or from hybridization and introgression of mitochondrial DNA (mtDNA) from one species into another (Avise et al., 1987). The co-occurrence of discontinuous mtDNA lineages at a given geographic site (‘category II’ of Avise et al., 1987) is not common. This has nevertheless been observed in two other Scombridae species – the bigeye tuna Thunnus obesus (Durand et al., 2005; Gonzalez et al., 2008) and the carite, Scomberomorus brasiliensis (Gold et al., 2010) – as well as in a few other pelagic fishes (Magoulas, Tsimenides & Zouros, 1996; Rosel & Block, 1996; Nesbøet al., 2000; Graves & McDowell, 2003; Viñas, Alvarado Bremer & Pla, 2004; Rohfritsch & Borsa, 2005), and is generally thought to result from secondary contact between formerly geographically isolated populations, although an alternative hypothesis of sympatric divergence has been also proposed. Assuming a mutation rate of approximately 10% per million years for the control region (Bowen et al., 2006), intraspecific net divergences among subclades within western Pacific S. commerson (9.0–11.8%) would imply that the lineages started to diverge between c. 450 000 and c. 590 000 years ago. This dating designates the lowering of the sea level within the last five or six Milankovitch glacial cycles as a possible cause for the geographic isolation of S. commerson populations in the Indo-West Pacific region. Secondary contact would have occurred during one or another of the subsequent rises in sea level.

Do migratory capabilities enhance geographic structure inS. commerson?

The present survey of genetic variation in S. commerson revealed deeply divergent mitochondrial clades associated with strong geographic structure at both mitochondrial and nuclear DNA loci, not only at the scale of the geographic distribution of the species, but also at the regional scale. At the Indo-Pacific scale, highly divergent clades in S. commerson and correlated morphological differences are indicative of cryptic species. At the regional scale, our results showed the occurrence of independent populations within the IMPA, confirming Sulaiman & Ovenden's (2009) preliminary results, and also within the south-western Pacific region. Despite having a pelagic lifestyle in both adults and larvae, the degree of geographic differentiation in S. commerson was much higher than generally observed in pelagic species, with the notable exceptions of D. russelli (Table 5). In particular, this degree of geographic partition contrasts with the patterns generally observed in other Scomberomorus species, where little or no genetic variation has been detected across the distribution (Table 6). Although genetic differences were evident between populations of both Scomberomorus munroi and Scomberomorus queenslandicus sampled along the north-eastern Australian shores (Begg, Keenan & Sellin, 1998), the magnitude of those differences as expressed by Wright's FST was the same as that in S. commerson sampled in the Persian Gulf and Oman Sea, but still one order lower than FST values for S. commerson in the tropical south-western Pacific (Table 6).

It might be sensible to assume that the extreme migrating ability of pelagic fishes entail wide-scale geographic homogeneity in allele frequencies. Although S. commerson occurs in inshore waters, and presumably does not cross large expanses of ocean, as do tunas and billfishes, the level of geographic difference observed at the regional scale in S. commerson (present results) is striking: this suggests that migrating ability might rather be associated with an increased potential for homing, and hence for reproductive isolation.

ACKNOWLEDGEMENTS

We thank P.H. Barber and J.R. Ovenden for encouragement and stimulating discussions, and two anonymous referees for suggesting improvements for the final article. Samples from New Caledonia were provided by our colleagues M. Leopold and D. Ponton. V. P. Buonaccorsi kindly provided the original FST values for Scomberomorus maculatus. The study was funded by UR128 and UR227 of the Institut de recherche pour le développement and by Programme d'évaluation des ressources marines de la zone économique exclusive de Nouvelle-Calédonie (ZoNéCo), Nouméa, New Caledonia.

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