Molecular analyses indicate homogenous structure of abalone across morphologically different Haliotis rubra collections in South Australia.ABSTRACT It is widely accepted that the identification and management of abalone populations is critical for the ongoing sustainability of the fishery. However management of the endemic blacklip abalone throughout the Australian states generally occurs over large spatial scales and in some instances, across morphologically different populations on a finer scale. In this study, we use molecular techniques to show that blacklip abalone from seven sampling sites (of which four sites were characterized by stunted abalone growth patterns) in South Australia were genetically homogenous. Ten microsatellite loci and tire composite restriction fragment length polymorphisms from the ND3/COIII mtDNA region showed high levels of genetic diversity across the collections, irrespective of growth characterization. Both molecular techniques also revealed a concordant outcome; no significant hierarchical variance was detected across geographically close, yet morphologically different collections (microsatellite [[PHI].sub.ST] = 0.002; mtDNA [[PHI].sub.ST] = 0.010). Pair-wise [F.sub.ST] estimates were also low and often negative for the microsatellites ([F.sub.ST] = -0.002 0.007) and mtDNA markers ([F.sub.ST] = -0.023-0.011). Our current findings support the hypothesis that blacklip abalone sampled here from "stunted" and "non-stunted" collections in the southern zone of the South Australian fishery ate genetically similar. KEY WORDS: blacklip abalone, Haliotis, DNA, mtDNA, microsatellites, stunted, population differentiation INTRODUCTION Australian abalone fisheries ate based primarily on the endemic blacklip abalone, Haliotis rubra (Leach, 1814). This species is distributed widely along the southern Australian rocky coastline; from New South Wales to Western Australia, including Tasmania (Geiger 2000, Geiger & Poppe 2000). A key issue for ensuring sustainable blacklip abalone fisheries in Australia is identifying specific tools (be they molecular, meristic, morphological, reproductive, or life-history based) that can be used to provide advice on the appropriate scale at which blacklip populations should be managed (Shepherd & Brown 1993, Prince 2005). More widely Haliotis species, which often live in population aggregations on varying spatial scales, form the core of abalone fisheries in many countries (Shepherd & Brown 1993). We believe therefore, that the development and testing of tools and approaches for recognizing abalone populations across spatial scales may have applications in abalone fisheries throughout the world. In Australia, management of blacklip fisheries occurs over large spatial scales (from 100-1,000 km) on a state-by-state basis through individual quotas, minimum harvest lengths and total allowable catches (Prince & Shepherd 1992). However, such wider scale management may compromise the sustainability of blacklip stocks by failing to account for spatial variability and differences in biological attributes (Saunders et al. 2008). Evidence suggests that abalone larvae generally disperse only short distances with adults unlikely to more lar after settlement (Prince et al. 1987, McShane et al. 1988, Nash 1992). There is also a high degree of variability in growth rates, fecundity, and size at sexual maturity among blacklip stocks (Shepherd & Hearn 1983, McShane et al. 1988, Worthington et al. 1995). Blacklip abalone are found in rocky coastline environments ranging from sheltered areas with lower wave action to areas, which are more exposed to the elements. Within these environments, the distribution of H. rubra is relatively continuous except where bays or sandy beaches intervene into the coastline areas (Shepherd & Brown 1993). The South Australian fishery consists of typical abalone habitat and includes all coastal waters of South Australia east of Meriden 139[degrees]E (with the exception of the Coorong and the waters inside the Murray River mouth) (Saunders et al. 2008). The spatial scale of management in the South Australian abalone fishery has decreased because of recognition of localized variability in abalone populations (Saunders et al. 2008). In the southern zone (SZ) of this fishery, harvest of abalone is managed according to a reduced minimum legal length of 110 mm (shell length); this is 15 mm smaller than that in the rest of the fishery (Saunders et al. 2008). In the SZ fishery, there are separately managed 'fish-down' (FDA) areas within which the blacklip abalone ate considered "stunted." Environmental conditions in these areas that affect access to food sources, coupled with density dependent factors, are believed to result in blacklip abalone which are smaller in size. Saunders et al. (2008) suggest that dense aggregations of abalone in sheltered areas grow more slowly compared with those in exposed areas. This is a result of less drift algae being available to the abalone and therefore the animals in these areas do not have access to the same level of food as blacklip abalone in other areas of the fishery (Saunders et al. 2008). Saunders et al. (2008) observed significant differences in a morphometric ratio (based on shell length and height) in blacklip populations from the SZ area of the fishery. The authors suggest that this morphometric marker could be used asa tool to identify individual abalone populations (Saunders et al. 2008). However, whereas morphometric variation is increasingly being used as a tool to help identify separate populations of marine species (Cadrin 2005), more widely it is believed that environmental conditions induce such spatial differences (Swain & Foote 1999, Swain et al. 2005). Moreover, missing from these approaches, is consideration of whether populations individually classified by biological or morphometric methods actually reflect ecological populations that are self sustaining (Day & Shepherd 1995, Berryman 2002). Populations might show differences in morphology, without being reproductively isolated. Recently, a reciprocal transplant experiment using blacklip abalone within the South Australian fishery, demonstrated that spatial variation in abalone growth and morphology was a plastic response to localized environmental conditions, not a genetic difference between conspecifics (Saunders et al. 2009). Observations such as this suggest that gene flow among blacklip populations is likely to be present particularly across finer spatial scales. Within the marine environment, abalone gametes have the opportunity to intermix, depending on spatial distances. Given the difficulties of tracking tiny abalone larvae over potentially large distances, examination of molecular markers therefore offers a potential tool to study this sharing of genes and give insights into the genetic relationships among blacklip populations. Molecular markers and genetic variation in H. rubra collections from Australian waters has been assessed using allozymes, random amplified polymorphic DNA (RAPDs), mitochondrial DNA (mtDNA) and genotypic repeat sequences (i.e., microsatellites). Studies have reported conflicting results (Conod et al. 2002, Evans et al. 2004, Temby et al. 2007), but have generally revealed restricted gene flow among populations in different Australian states with higher levels of homogeneity within state waters (albeit depending on the level of spatial scales being tested) (Miller et al. 2009). In contrast, genetic analyses among morphologically distinct populations of the one species are not generally undertaken. In the current study, which was part of a wider habitat and morphological examination of blacklip abalone in the SZ fishery (Mayfield & Saunders 2008), we test the hypothesis that H. rubra collections in the SZ are not genetically different irrespective of growth classification. Blacklip abalone in this fishery come from areas in which the animals are considered stunted as compared with nonstunted areas--stunted blacklip abalone have a smaller maximum length when compared with other individuals (Wells & Mulvay 1995, Saunders et al. 2008). We use mitochondrial DNA (mtDNA) and nuclear microsatellite markers to test our hypothesis. Microsatellites are codominant markers that display high levels of polymorphism and are used widely for population differentiation studies in fisheries and aquaculture (O'Connell & Wright 1997, Appleyard et al. 2002, Appleyard & Ward 2006). In contrast, mtDNA is a haploid marker used for investigating population genetic diversity and species identification but one which is more sensitive to drift and bottleneck effects (Brown et al. 1979, Avise 1994, Moore 1995). Notably, this is also the first larger scale study to use a relatively modest set of newly identified microsatellite loci developed for H. rubra (see Baranski et al. 2006) in a population context. MATERIALS AND METHODS Samples and DNA Extraction Samples collected across seven collection sites along the South Australian coastline were provided as frozen (-20[degrees]C) blacklip muscle tissue by colleagues at the South Australian Research and Development Institute (SARDI) (see Fig. 1, Table 1). These samples were from whole juvenile animals (70-80 mm standard length) collected over an area of<10[m.sup.2] at each location. Up to 48 samples per collection site were used in this study. [FIGURE 1 OMITTED] We extracted total genomic DNA from approximately 20 mg of foot muscle tissue using Wizard SV96 Genomic DNA Purification Systems (Promega; USA) as per the manufacturer's instructions, except for elution volumes, which we reduced to 200 gL. Remaining tissue samples were stored, along with an aliquot of the extracted DNA, at -80[degrees]C. We diluted the genomic DNA for each individual to 100 ng [micro][L.sup.-1] for mtDNA applications and a second aliquot of 10-25 ng [micro][L.sup.-1] was used for microsatellite loci. These aliquots were stored at 4[degrees]C for working applications. MtDNA-RFLP Digests We used the polymerase chain reaction (PCR) to amplify an approximately 1,500 base pair (bp) gene fragment from the mtDNA genome. This fragment (ND3/COIII region) consists of a large portion of the NADH subunit 3 gene, the complete COIII gene (Sweijd 1999) and five transfer RNA genes (Maynard et al. 2005). The primers used were P3: 5'-AAAGTGATCACAGAA ATGACCCG-3' and P4: 5'-GATAAGAAGAAAGCAAAGA ACCCC-3' (Sweijd 1999). PCR reactions were undertaken in an Eppendorf Mastercycler EP gradient thermal cycler (Eppendorf; Germany) and a Perkin Elmer GeneAmp System 9600 thermal cycler (Applied Biosystems; USA). Reactions consisted of 1 [micro]L of 10 mM dNTPs (Promega; USA), 3 [micro]L of 25 mM Mg[Cl.sub.2] (Applied Biosystems), 5 [micro]L of l0 x AmpliTaq Gold Buffer (Applied Biosystems), 1 [micro]L of 10 [micro]M P3 and P4 (Geneworks; South Australia), 0.25 [micro]L of AmpliTaq Gold (Applied Biosystems) and 100-150 ng of template DNA, adjusted to a final volume of 50 [micro]L with dd[H.sub.2]O. The PCR cycling conditions were as follows: initial denaturation at 93[degrees]C for 10 min, 35 cycles of 93[degrees]C for 30 s, 60[degrees]C for 1 min, and 72[degrees]C for 2 min 30 s. A final extension cycle of 72[degrees]C for 10 min was followed by an indefinite 4[degrees]C hold. Restriction fragment length polymorphism (RFLP) haplotypes were assessed by digesting the ND3/COIII fragment with five restriction enzymes (DdeI, DpnII, HaeIII, MspI, and RsaI (New England Biolabs; USA) as described by Conod et al. 2002). Enzyme digests were undertaken separately and fragments were run on a 2.5% TBE (Tris, Boric acid, EDTA) buffer agarose gel containing ethidium bromide at 120V for 1 h against a Hyperladder size standard (Bioline; USA). Fragments were visualized under UV light and photographed with a digital camera to enable scoring of haplotypes (similar to that described by Conod et al. 2002). All haplotypes were scored independently by two scorers and cross checked for congruence prior to statistical analyses. Microsatellite DIVA Amplification We examined variation at ten H. rubra microsatellite loci using two PCR multiplex reactions. These loci (Hrubl0.G02, 10.E02, 12.G07, 1.D04, 4.E06, 16.G01, 8.A09a, 9.F11, 2.G01; referred to as Loci 1.3, 5.... 10 respectively) were from Baranski et al. (2006) and 4. All (Locus 4) was from M. Baranski (pers. comm.). Each 25 [micro]M multiplex per individual was performed in ah Eppendorf Mastercycler (as earlier). Amplifications consisted of 1 [micro]L of 10 mM dNTP's (Promega), 2.5 [micro]L of 25 mM Mg[Cl.sub.2] (Applied Biosystems), 2 [micro]L of 10 x AmpliTaq Gold Buffer (Applied Biosystems), 1.2-1.4 [micro]M of forward and reverse primers (forward primers were fluorescently labeled with either FAM, HEX, TET and PET dyes; Applied Biosystems), 0.25 [micro]L of AmpliTaq Gold (Applied Biosystems) and 10-15 ng of template DNA, adjusted to a final volume of 25 [micro]L with dd[H.sub.2]O. The PCR cycling conditions were: initial denaturation at 93[degrees]C for 10 min, 40 cycles of 93[degrees]C for 30 s, 55[degrees]C for 1 min 30 s, and 72[degrees]C for 2 min. A final extension cycle of 72[degrees]C for 10 min was followed by an indefinite 4[degrees]C hold. From each master mix, 1 [micro]L of amplified products were diluted in a mix of HiDi Formamide (Applied Biosystems) and dd[H.sub.2]O and denatured at 94[degrees]C for 2 min. Samples were run on an ABIPrism 3100 Genetic Analyser against ah internal GeneScan-500 LIZ Size Standard (Applied Biosystems). We used GeneMapper v.3.7 (Applied Biosystems) software to set the parameters and bin ranges that enabled routine scoring. For each collection, samples were scored as they were run; as with the mtDNA RFLP haplotypes, genotypes were independently scored by two researchers. Genotypes were checked again after all samples had been run for all loci. Statistical Analysis Within Collection Diversity For mtDNA, we identified the most common haplotype from each restriction digest by the letter A. Variable restriction patterns, per enzyme digest, were subsequently designated alphabetically. The composite haplotypes for each individual consisted of five letters. In the analysis, we only used those individuals for which data from all five restriction enzymes were available. We used standard RFLP coding (based on the presence/absence of restriction sites). Composite haplotype data and restriction site information was then analyzed by ARLEQUIN vers3.1 (Excoffier et al. 2006) to calculate unbiased haplotype diversity (h; Nei 1987) and nucleotide diversity ([pi]; Tajima 1983, Nei 1987). Haplotype diversity potentially ranges from zero (all individuals share a common haplotype) to one (all individuals have different haplotypes). For the microsatellite analyses, allele frequencies, numbers of alleles ([N.sub.alleles]), observed ([H.sub.o]) and expected heterozygosities ([H.sub.e]) for each collection and locus were calculated in FSTAT v.2.9 (Goudet 2001). We used the rarefaction approach in FSTAT to calculate "allelic richness" (A); this enables comparisons across collections of differing sample sizes. Deviations from Hardy-Weinberg Equilibrium (HWE) at each locus and each collection were calculated in ARLEQUIN using a Markov chain approach. MICRO-CHECKER v2.2.1 (van Oosterhout et al. 2003) assessed the potential for large allele dropout, scoring errors due to stuttering and the potential for null alleles by comparing observed and expected homozygote genotype frequencies and associated bin sizes. We assessed linkage disequilibrium among loci using Fisher exact tests in GENEPOP vers3.3 (Raymond & Rousset 1995). Among Collection Diversity Analysis of molecular variance (AMOVA), based on analogues of Weir & Cockerham (1984) F parameters, was used to investigate differentiation among stunted and nonstunted collections in ARLEQUIN (undertaken separately for the two marker types). In these instances, the microsatellite allele frequencies and mtDNA haplotype frequencies from blacklip abalone sourced from RR, AR, RB, and GB were grouped together and those from CN, N[O.sub.2], and MP were grouped together. [[PHI].sub.ST], [[PHI].sub.CT], and [[PHI].sub.SC] (the analogues of [F.sub.ST], [F.sub.CT], [F.sub.SC]), were obtained as the estimated variance components resulting from differences among hierarchical groupings divided by the estimated total variance. Collection differentiation, based on multilocus [F.sub.ST] statistics (Wright 1951, Weir & Cockerham 1984), was calculated and examined across all loci and pair-wise comparisons, in ARLEQUIN. Variation in mtDNA composite haplotype frequencies in combined stunted and nonstunted collections was assessed using standard Monte-Carlo chi-square approaches in the program CHIRXC (Zaykin & Pudovkin 1993), with 10,000 randomizations of the data being used to estimate P values. The significance levels for multiple tests were adjusted following a sequential Bonferroni approach (Rice 1989). RESULTS Within Collection Diversity Among the three hundred and three blacklip individuals, a high level of haplotype diversity was observed in the ND3/ COIII region (Table 2). Twenty-five different composite haplotypes were observed although most of the animals displayed one of four haplotypes (AAAAA, BBBCA, AAAAB, and AAABA). The blacklip collections had an average haplotype diversity of 0.753 (SE [+ or -] 0.017) and nucleotide diversity of 0.137 (SE [+ or -] 0.003). Within collection haplotype diversity was high, ranging from 0.711-0.837, and nucleotide diversity was less than 0.150 in all collections. For the microsatellite loci, between 272 and 301 individuals were genotyped, depending on the locus (data not shown). Whereas 10 loci were screened, a large number of individuals did not amplify at L4 (from Master Mix 1). Subsequently, this locus was omitted from further analyses. Tests for linkage disequilibrium (following Bonferroni correction) indicated no significant comparisons in any collection pair. The loci were considered independent, as no two markers were associated in all collections. All loci displayed moderate to high levels of polymorphism. The average number of alleles per locus ranged from 13 (L10) to 35 (L9) (Table 3). Allelic richness varied considerably among the loci (11.01-26.94 depending on the locus). L1 (0.962) and L2 (0.498) displayed the highest and lowest [H.sub.o] values across the seven collections respectively. Average [H.sub.o], despite the relatively low sample sizes, was 0.750 (across the seven collections). Average Ho across the nine loci per collection was at least 90%. Approximately 50% ofgenotypic tests across the nine loci in the seven collections did not conforto to HWE (following Bonferroni correction; Table 3). In most instances, this was caused by a lack of heterozygotes. Among Collection Diversity An overall exact test of collection differentiation based on mtDNA haplotype frequencies across the seven collections was not significant (P = 1.000). [F.sub.ST] comparisons among the seven collections were all small and often negative (Table 4). Following this testing, we grouped the composite haplotypes frequencies from the four stunted areas and compared these to the combined haplotype frequencies from the three nonstunted areas; we also observed no significant differentiation ([chi square] 24 = 18.991, P = 0.881) between the two groups. Likewise, there were no significant allele frequency differences observed at the microsatellite loci across the seven collections (exact test, P = 1.000) and there were also no significant pair-wise microsatellite [F.sub.ST] comparisons observed among the seven collections (Table 4). Hierarchical AMOVA analyses between the stunted (consisting of RR, AR, RB, GB collections) and the nonstunted group (consisting of CN, NO2, MP collections) and collections within each of the groups demonstrated very small group variance components for both marker types (Table 5). The mtDNA variance (100%) and <0.3% of the microsatellite variance was attributed to among collection (not group) differences (Table 5). DISCUSSION The focus of this study was to test if blacklip abalone sampled from arcas classified as stunted (FDA areas) and nonstunted (nonFDA arcas) in the SZ fishery were not genetically different. This is important as different total allowable commercial catch (TACC) management arrangements exist within the SZ fishery based on FDA and nonFDA areas (Mayfield & Saunders 2008). Both mtDNA and microsatellite variation revealed no significant genetic differentiation among the collections based on the samples screened in this study. We therefore could not reject the null hypothesis of genetic homogeneity across samples in the FDA and nonFDA areas. Genetic Diversity In Collections We observed high levels of mitochondrial and nuclear genetic diversity in all collections. Average mtDNA haplotype diversity in the current study (h = 0.753) was very similar to that obtained by Conod et al. (2002) for four Tasmanian blacklip collections (h = 0.708), and that observed by Li et al. (2006) from a small pilot study on blacklip from several additional South Australian sites (h = 0.804). Observed heterozygosity levels for our nine microsatellite loci were generally high, although the levels were not consistent across all loci. Similar levels of variation, albeit based on different microsatellite loci, have previously been reported for blacklip abalone (Huang et al. 2000, Conod et al. 2002, Evans et al. 2004). We detected heterozygote deficiencies in the microsatellites. These deviations from expectations are likely to be a result of relatively small sample sizes in each collection (i.e., less than 50 individuals) coupled with high allele numbers per locus (e.g., average number of alleles across the seven collections at L9 was 35). Additionally, MICRO-CHECKER indicated the probability of null alleles (which can also cause deviations from expectations), however the statistical testing did not indicate the presence of dinucleotide stuttering and or large allele dropout. Therefore we need to be careful in our interpretation of the allelic and genotypic data per se, however as the collection sizes (albeit low) were generally consistent across each locus, deviations from HWE are unlikely to affect the overall collection comparisons given the alleles were scored across all collections in the same manner. Homogeneity Between Stunted and Nonstunted Collections The low and nonsignificant [F.sub.ST] values observed among all the SZ collections (irrespective of FDA or nonFDA classification) indicate the possibility of processes acting to maintain genetic homogeneity across these morphologically different, yet geographically close areas. After fertilization, blacklip eggs are negatively buoyant, but after hatching the larvae migrate to the surface for an active swimming phase (Shepherd & Brown 1993). Larvae can exist in the upper water column for up to 10 days (although this is influenced by water temperatures) and hydrodynamic conditions determine dispersal rates (Shepherd & Brown 1993, Hamm & Burton 2000), until they settle on suitable habitat (McShane et al. 1988). Because of the sedentary nature of the adults, it is more likely that larval dispersal is maintaining gene flow rather than adult movements into neighboring areas. We did not detect the same level of genetic subdivision reported by Huang et al. (2000) in blacklip populations based on microsatellites ([[PHI].sub.ST] = 0.067, P < 0.001) and RAPD loci [[PHI].sub.ST] = 0.074, P < 0.001) or Temby et al. (2007) from more fine scale H. rubra sampling sites in Tasmania ([F.sub.ST] = 0.021, P < 0.050). However, we believe that differences in sample sizes, choice of molecular markers and the number of loci used can contribute to the variation in study outcomes. Temby et al. (2007) detected low but significant subdivision among 18 blacklip collections in south-east Tasmania, based on three microsatellite loci. As with our study, Temby et al. (2007) used small sample sizes per collection (up to n = 30) although many more collections were analyzed. In this current study, we used nine microsatellite loci and a mtDNA marker. In contrast, our results are consistent with other previously reported studies for blacklip populations in Tasmania and South Australia (Conod et al. 2002, Evans et al. 2004, Li et al. 2006) in which no significant genetic heterogeneity was detected across either fine or wider scale spatial collections. The microsatellite and mtDNA markers in our current study suggest a lack of heterogeneity across individuals sampled from FDA and nonFDA areas in the southern zone of the South Australian abalone fishery. The genetic evidence from this research indicates that blacklip abalone populations could possibly be managed as one unit within the SZ, irrespective of growth classification. A lack of significant heterogeneity as suggested by our data demonstrates some level of recruitment of individuals among collection sites may be occurring. Currently, management of most abalone fisheries conform to this model and are managed on a broadscale (100s-1,000s km). However, this is at odds with more recent abalone population theory that suggests abalone form discrete self-sustaining populations (that are highly variable in their life-history characteristics and or can display morphological differences) and may need to be managed on a more finer scale (McShane et al. 1988, Worthington et al. 1995, Saunders et al. 2008). Notably, broad-scale management could leave some populations vulnerable to overfishing. Contrasts Between Genetic and Morphological Markers The discrepancy between the scale of spatial variation detected by genetic and morphological markers (Saunders et al. 2008) in these blacklip abalone collections may result from only a few migrant larvae per generation maintaining genetic homogeneity (Slatkin 1985, Miller & Shanks 2004). Additionally, it is highly likely that localized environmental influences (i.e., lower wave action, less algae as food source) play an important role in developing morphologically discriminated collections (Saunders et al. 2008) and variances in the expression of morphological traits, through genetic x environment interactions (G x E effects). Although these morphological characteristics may be environmentally induced (Swain & Foote 1999, Swain et al. 2005, Saunders et al. 2008), such environmental influences do not necessarily prevent gene flow (i.e., genetic homogeneity) among blacklip collections, assuming that gametes are still compatible and have the opportunity to mix. Our genetic results are further supported by the findings of Saunders et al. (2009), which showed that transplanted blacklip abalone grew differently to their native site controls as a result of a phenotypic response to their local environment. Morphological plasticity was not caused by genetic differences among populations (Saunders et al. 2009). Whereas we were unable to reject the null hypothesis of genetic homogeneity for blacklip from the FDA and nonFDA areas of the SZ fishery, there are several caveats around this finding. The failure to disprove the null hypothesis does not mean that genetic heterogeneity does not exist; only that we did not detect it in the current study. It may be that our inability to reject the null hypothesis is related to the power of the genetic markers used (Graves & McDowell 2003). However, in this current study, we screened variation at two different molecular markers and the sample sizes in our seven collections were consistent. Despite this, the inability of both markers to detect genetic differences could still be related to the relatively short period of time, which may be required for significant genetic differences to accumulate among the areas or the lack of suitability of the spatial scales over which we were analyzing genetic connectivity (Temby et al. 2007). Overall Our findings show that the blacklip abalone sampled here from "stunted" and "non-stunted" areas displayed good levels of genetic variability in each collection alongside relative homogeneity among the collections. We further demonstrated the concordance between the mtDNA and microsatellite markers. Unlike the morphological data from blacklip abalone sampled from stocks in the same SZ areas (Saunders et al. 2008), the genetic data detected no significant difference in spatial scales for samples from the "stunted" and "non-stunted" areas. This is in strong contrast to recent, increasing calls for recognizing the spatial and morphological variability among abalone populations, and managing these populations according to their specific life-history characteristics and environmental influences. Consequently, in combination, we believe that a holistic approach, that encompasses genetic, morphological and biological aspects, is required for assessment and management of abalone populations within this fishery. ACKNOWLEDGMENTS The authors thank their colleagues Thor Saunders and Stephen Mayfield at the South Australian Research and Development Institute (SARDI), for collecting samples (and obtaining sampling authorities and ethics approvals where necessary) and providing valuable insights into the biological characteristics of the sampled abalone and Robert Ward, Peter Kube and Greg Coman, for their critical reviews on earlier drafts of this manuscript and Karen Miller for the helpful and insightful comments on this manuscript. This research was part of a Fisheries Research and Development Corporation (FRDC) grant (Project Number 2004/019, Principal Investigator Stephen Mayfield) awarded to SARDI. LITERATURE CITED Appleyard, S. A., R. D. Ward & R. Williams. 2002. Population structure of the Patagonian toothfish around Heard, McDonald and Macquarie Islands. Ant. Sci. 14:364-373. Appleyard, S. A. & R. D. Ward. 2006. 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Oxford: Fishing New Books. pp. 407-426. Prince, J. D., T. L. Sellers, W. B. Ford & S. R. Talbot. 1987. Experimental evidence for limited larval dispersal of haliotid larvae (genus Haliotis; Mollusca: Gastropoda). J. Exp. Mar. Biol. Ecol. 106:243-263. Raymond, M. & F. Rousset. 1995. Population genetics software for exact tests and ecumenicism. J. Hered. 86:248-249. Rice, W. R. 1989. Analyzing tables of statistical tests. Evolution Int. J. Org. Evolution 43:223-225. Saunders, T. M., S. D. Connell & S. Mayfield. 2009. Differences in abalone growth and morphology between locations with high and low food availability: morphologically fixed or plastic traits? Mar. Biol. 156:1255-1263. Saunders, T. M., S. Mayfield & A. A. Hogg. 2008. A simple, cost-effective, morphometric marker for characterising abalone populations at multiple spatial scales. Mar. Freshw. Res. 59:32-40. Shepherd, S. A. & L. D. Brown. 1993. What is ah abalone stock: Implications for the role of refugia in conservation? Can. 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PhD thesis. Tajima, F. 1983. Evolutionary relationship of DNA sequences in finite populations. Genetics 105:437-460. Temby, N., K. Miller & C. Mundy. 2007. Evidence of genetic subdivision among populations of blacklip abalone (Haliotis rubra Leach) in Tasmania. Mar. Freshw. Res. 58:733-742. van Oosterhout, C., W. F. Hutchinson, D. P. M. Wills & P. F. Shipley. 2003. MICRO-CHECKER.: molecular ecology and fisheries genetics laboratory, Hull, United Kingdom: The University of Hull. Weir, B. S. & C. C. Cockerham. 1984. Estimating F-statistics for the analysis of population structure. Evolution Int. J. Org. Evolution 38:1358-1370. Wells, F. E. & P. Mulvay. 1995. Good and bad fishing areas for Haliotis laevigata: a comparison of population parameters. Mar. Freshw. Res. 46:591-598. Worthington, D. G., N. L. Andrew & G. Hamer. 1995. Covariation between growth and morphology suggests alternative size limits for the blacklip abalone, Haliotis rubra, in New South Wales, Australia. Fish. Bull. (Wash. D. C.) 93:551-561. Wright, S. 1951. The genetical structure of populations. Ann. Hum. Genet. 15:323-354. Zaykin, D. V. & A. I. Pudovkin. 1993. Two programs to estimate significance of [chi square] values using pseudo-probability tests. J. Hered. 84:152. SHARON A. APPLEYARD, * NATASHA A. CARR AND NICHOLAS G. ELLIOTT CSIRO Food Futures Flagship, Marine and Atmospheric Research, GPO Box 1538, Hobart, Tasmania 7001, Australia * Corresponding author. E-mail: Sharon.Appleyard@csiro.au
TABLE 1.
Sampling locations for H. rubra (n = 48 for all sites)
in the SZ South Australian fisher. Samples were collected
in March 2006. Bold indicates a "stunted" site.
Site Description Latitude
Cape Northumberland CN 38[degrees]03.6' S
Ringwood Reef RR 37[degrees]31.9' S
Acis Reef AR 38[degrees]02.8' S
Number 2 Rocks NO2 37[degrees]48.8' S
Red Rock RB 37[degrees]54.6' S
Bay
Middle Point MP 38[degrees]02.5' S
Gerloffs Bay GB 37[degrees]55.7' S
Site Longitude
Cape Northumberland 140[degrees]39.7' E
Ringwood Reef 140[degrees]02.6' E
Acis Reef 140[degrees]37.9' E
Number 2 Rocks 140[degrees]19.5' E
Red Rock 140[degrees]23.2' E
Bay
Middle Point 140[degrees]37.0' E
Gerloffs Bay 140[degrees]24.4' E
TABLE 2.
RFLP analysis of the ND3/COIII mtDNA fragment based on 5
restriction enzymes (DdeI, DpnII, HaeIII, MspI, and RsaI)
showing the distribution of 25 composite haplotypes among
the seven H. rubra collections. RFLP success, haplotype
(h) and nucleotide ([pi]) diversity are also shown.
Collection
Haplotype CN RR AR NO2
1 AAAAA 21 18 16 21
2 BBBCA 8 5 16 13
3 AAAAB 3 3 5 7
4 AAABA 2 6 4 3
5 BBCCA 3 1 1
6 ABOCA 1 2 1 1
7 BABCA 1 2
8 AABAA
9 CCACA 1 1
10 AADBA 1
11 ABOCA
12 CBACA 1 2
13 AAAAC 1
14 ABAAA
15 ADAAA
16 BBACA
17 ACACA
18 ABOCA
19 ABOCA 1
20 ABOCA 1
21 BBBCD 1
22 BBBCE 1
23 BDABA
24 DAAAA 1
25 DBFCA 1
Total 44 42 44 47
RFLP 92% 88% 92% 98%
h 0.741 0.786 0.729 0.711
[pi] 0.135 0.131 0.144 0.130
Collection
Haplotype RB MP GB Total
1 AAAAA 21 13 17 127
2 BBBCA 12 10 10 74
3 AAAAB 5 3 5 31
4 AAABA 4 4 2 25
5 BBCCA 2 1 8
6 ABOCA 1 1 7
7 BABCA 3
8 AABAA 1 1
9 CCACA 3 5
10 AADBA 1
11 ABOCA 2 1 3
12 CBACA 3
13 AAAAC 1 2
14 ABAAA 2 2
15 ADAAA 1 l
16 BBACA 1 1 2
17 ACACA 1 1
18 ABOCA 1 1
19 ABOCA 1
20 ABOCA 1
21 BBBCD 1
22 BBBCE 1
23 BDABA 1 1
24 DAAAA 1
25 DBFCA 1
Total 48 41 38 303
RFLP 100% 85% 79%
h 0.738 0.837 0.727
[pi] 0.144 0.149 0.127
TABLE 3.
Summary statistics for microsatellite loci (L) screened in H. ruhra
collections. Mean sample size per locus (N), number of alleles
([N.sub.alleles]), allelic richness (A), heterozygosity observed
([H.sub.o]), and heterozygosity expected under equilibrium conditions
([H.sub.e]) are shown. Significant [H.sub.o] estimates following
Bonferroni correction are shown in bold.
Collection Statistic L 1 L 2 L 3
CN N 44 43 42
[N.sub.alleles] 32 22 31
A 24.58 16.59 23.20
[H.sub.o] 0.977 0.465 0.738
[H.sub.e] 0.967 0.917 0.946
RR N 39 36 38
[N.sub.alleles] 33 19 28
A 26.12 15.78 20.98
[H.sub.o] 1.000 0.417 0.842
[H.sub.e] 0.971 0.905 0.914
AR N 41 3R 40
[N.sub.alleles] 32 22 27
A 24.90 17.34 19.49
[H.sub.o] 0.927 0.553 0.650
[H.sub.e] 0.927 0.920 0.901
NO2 N 41 37 35
[N.sub.alleles] 29 20 23
A 23.41 17.18 18.67
[H.sub.o] 0.878 0.514 0.543
[H.sub.e] 0.964 0.927 0.896
RB N 43 42 39
[N.sub.alleles] 31 21 27
A 23.26 17.03 20.55
[H.sub.o] 1.000 0.452 0.718
[H.sub.e] 0.961 0.939 0.926
MP N 43 42 41
[N.sub.alleles] 33 27 39
A 25.00 20.13 27.82
[H.sub.o] 0.953 0.714 0.878
[H.sub.e] 0.966 0.943 0.958
GB N 44 43 37
[N.sub.alleles] 28 19 20
A 22.35 15.72 16.36
[H.sub.o] 1.000 0.372 0.595
[H.sub.e] 0.959 0.926 0.905
Average N 42 40 39
[N.sub.alleles] 31 21 28
A 24.23 17.11 21.01
[H.sub.o] 0.962 0.498 0.709
[H.sub.e] 0.965 0.925 0.921
Collection Statistic L 5 L 6 L 7
CN N 42 45 45
[N.sub.alleles] 25 22 18
A 21.40 18.40 13.90
[H.sub.o] 0.786 0.956 0.800
[H.sub.e] 0.963 0.940 0.851
RR N 39 42 40
[N.sub.alleles] 23 26 23
A 19.23 20.45 18.19
[H.sub.o] 0.718 0.929 0.875
[H.sub.e] 0.951 0.950 0.916
AR N 39 40 4l
[N.sub.alleles] 28 25 18
A 23.10 19.73 15.16
[H.sub.o] 0.538 0.850 0.829
[H.sub.e] 0.970 0.946 0.902
NO2 N 39 43 43
[N.sub.alleles] 27 23 18
A 22.03 18.64 14.21
[H.sub.o] 0.718 0.884 0.791
[H.sub.e] 0.960 0.941 0.883
RB N 43 44 44
[N.sub.alleles] 27 22 17
A 21.36 18.09 14.03
[H.sub.o] 0.698 0.932 0.841
[H.sub.e] 0.953 0.946 0.883
MP N 43 46 46
[N.sub.alleles] 27 23 17
A 21.62 18.27 13.34
[H.sub.o] 0.791 0.826 0.761
[H.sub.e] 0.950 0.943 0.849
GB N 39 44 44
[N.sub.alleles] 27 25 18
A 21.68 19.81 14.17
[H.sub.o] 0.667 0.909 0.841
[H.sub.e] 0.159 0.946 0.868
Average N 41 43 43
[N.sub.alleles] 26 24 18
A 21.49 19.06 14.71
[H.sub.o] 0.702 0.898 0.820
[H.sub.e] 0.958 0.946 0.879
Collection Statistic L 8 L 9 L 10
CN N 45 40 45
[N.sub.alleles] 25 35 15
A 19.76 27.25 12.12
[H.sub.o] 0.933 0.675 0.644
[H.sub.e] 0.948 0.979 0.889
RR N 42 39 42
[N.sub.alleles] 22 37 13
A 18.43 28.96 10.88
[H.sub.o] 0.881 0.769 0.643
[H.sub.e] 0.945 0.979 0.875
AR N 41 40 40
[N.sub.alleles] 26 32 12
A 21.21 24.85 9.95
[H.sub.o] 0.732 0.650 0.625
[H.sub.e] 0.954 0.965 0.843
NO2 N 43 40 43
[N.sub.alleles] 25 38 14
A 20.18 28.86 10.74
[H.sub.o] 0.907 0.725 0.535
[H.sub.e] 0.947 0.979 0.855
RB N 44 42 44
[N.sub.alleles] 26 36 12
A 20.66 26.87 10.01
[H.sub.o] 0.977 0.643 0.636
[H.sub.e] 0.949 0.975 0.860
MP N 46 46 46
[N.sub.alleles] 26 36 14
A 20.18 26.71 11.22
[H.sub.o] 0.935 0.609 0.587
[H.sub.e] 0.951 0.977 0.879
GB N 43 36 43
[N.sub.alleles] 23 30 14
A 17.71 25.07 12.15
[H.sub.o] 0.884 0.611 0.535
[H.sub.e] 0.937 0.971 0.880
Average N 43 40 43
[N.sub.alleles] 25 35 l3
A 19.73 26.94 11.01
[H.sub.o] 0.893 0.667 0.600
[H.sub.e] 0.947 0.975 0.869
TABLE 4.
Pair-wise FST values among the seven H. rubra collections based
on composite mtDNA-RFLP haplotypes (below the diagonal)
and multilocus microsatellite loci (above the diagonal). Negative
values are equal to zero. There were no significant FST pair-wise
comparisons following Bonferroni correction.
CN RR AR NO2 RB MP GB
CN -- 0.001 0.004 0.001 0.007 0.003 0.003
RR -0.008 -- 0.007 0.003 0.005 0.004 0.006
AR -0.008 0.011 -- 0.000 0.000 0.002 0.004
NO2 -0.017 -0.010 -0.009 -- 0.002 0.001 0.002
RB -0.011 -0.012 -0.004 -0.016 -- 0.001 0.002
MP -0.013 0.000 -0.019 -0.013 -0.009 -- -0.002
GB -0.022 -0.009 -0.012 -0.023 -0.014 -0.016 --
TABLE 5.
H. rubra hierarchical AMOVA based on mtDNA haplotypes and
microsatellite genotypes. Group 1 = RR, AR, RB, GB (stunted
collections); Group 2 = CN, NO2, MP (nonstunted collections).
P values are provided in parentheses.
mtDNA
Source of Variation Variance % Variation mtDNA
Among groups -0.006 -0.22 -0.002 (0.852)
Collections within
the groups -0.027 -0.98 -0.010 (0.865)
Within collections 2.750 101.20 -0.010 (0.908)
Microsatellite [F.sub.statistics]
Source of Variation Variance % Variation Microsatellite
Among groups -0.001 -0.05 -0.001 (0.686)
Collections within
the groups 0.010 0.29 -0.003 (0.205)
Within collections 3.552 99.76 -0.002 (0.100)
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