Introduction

Donax deltoides (Vereroida: Donacidae), more commonly known as the ‘pipi’, is a benthic marine bivalve commonly found in the swash zone on high energy beaches in south eastern Australia. The species is currently harvested by recreational fishers across most of its range with commercial fisheries also operating in regions of South Australia, Victoria and New South Wales (Ferguson and Mayfield 2006; Murray-Jones 1998). In recent years localized population declines have been observed in the respective states where fisheries currently operate reflecting the species’ vulnerability to over-exploitation (Ferguson and Mayfield 2006; Lewis and Scarpaci 2010; Murray-Jones 1998; Murray-Jones and Johnson 2003). In order to implement sustainable management strategies for the fishery an improved understanding of the species biology, ecology and genetics is needed. In particular, a comprehensive assessment of population genetic structure will provide managers with an effective spatial framework for managing the fishery and baseline data for future population monitoring. Prior to this study, effective genetic markers for population genetic assessments of D. deltoides were not available.

The 454 next generation sequencing platform was used to identify microsatellite markers for D. deltoides. Approximately 10 μg of genomic DNA was extracted from muscle tissue from a single D. deltoides specimen using a QIAGEN DNA Easy kit (Qiagen). DNA was subsequently processed by the Australian Genome Research Facility where it was nebulized, ligated with 454 sequencing primers and tagged with a unique oligo sequence allowing sequences to be separated from pooled species DNA sequences using post-run bioinformatic tools. The DNA sample was analyzed using high throughput DNA sequencing on 1/16 of a 70 × 75 mm PicoTiterPlate using the Roche GS FLX (454) system (Margulies et al. 2005). A total of 79,905 reads were obtained from the analysis, from which 721 unique sequence contigs possessing microsatellite motifs were identified using the software GDD (Meglécz et al. 2010). Primer3 (Rozen and Skaletsky 2000) was used to design optimal primer sets for each unique contig where possible, with a total of 90 contigs found to possess optimal priming sites. A selection of 40 contigs was used for subsequent analysis, 25 of which consisted of di-nucleotide repeats and 15 consisting of tri-nucleotide repeats.

Loci were screened for polymorphism using template DNA from eight individuals, representing 3 wild populations from south eastern Australia. These included two sites from Victoria (Nelson, −38.067 141.014, and Venus Bay, −38.706 145.811) and one site from South Australia (Coorong, −35.910 139.395). Loci were pooled into 10 groups of four, labelled with unique fluorophores (FAM, NED, VIC, PET) and co-amplified by multiplex PCR using a Qiagen multiplex kit (Qiagen) and an Eppendorf Mastercycler S gradient PCR machine following the protocol described by Blackett et al. (pers comm). Genotyping was subsequently performed using an Applied Biosystems 3730 capillary analyzer and product lengths were scored manually and assessed for polymorphisms using GeneMapper version 4.0 (Applied Biosystems). From 40 loci a total of 20 were found to be polymorphic (Table 1), 10 were monomorphic and nine failed to amplify.

Table 1 Primers sequences and characteristics of 20 microsatellite loci isolated from Donax deltoides
Full size table

A subset of 12 polymorphic loci were selected, pooled into two groups for multiplexing based on observed locus specific allele size ranges and further characterized using 30 individuals from the Venus Bay population. Microsatellite profiles were again examined using GeneMapper version 4.0 and alleles were scored manually. The Excel Microsatellite Toolkit (Park 2001) was then used to estimate expected (H E ) and observed (H O ) heterozygosities and number of alleles (NA), while examination of Hardy–Weinberg proportions (HWE), the inbreeding coefficient (F IS ) and linkage disequillibrium between all pairs of loci were conducted using GENEPOP version 4 (Raymond and Rousset 1995). Significance values were adjusted to allow for multiple statistical tests using Bonferroni corrections where necessary (Rice 1989). Finally to check for null alleles and scoring errors all loci were assessed using MICRO-CHECKER (Van Oosterhout et al. 2004). The frequency of null alleles per locus was obtained using the ‘Brookfield 1’ formula as null homozygotes were not observed (Brookfield 1996).

Majority of loci were characterized by moderate to high genetic variation, with an average of 7.3 alleles per locus (range = 2–14 alleles) and heterozygosity estimates ranging between 0.189 and 0.860 (mean = 0.633). Marker independence was confirmed as linkage disequilibrium analyses indicated no significant linkage between loci. Only two loci, Ddel6 and Ddel32, were found to deviate significantly from Hardy–Weinberg Equilibrium (P = 0.002 and P < 0.001, respectively; Table 1) and display significant F IS estimates (F IS  = 0.2989, P = 0.0127 and F IS  = 0.2989, P = 0.0127, respectively; Table 1), suggesting heterozygote deficiencies. MICRO-CHECKER analyses identified evidence of null alleles at three loci, Ddel6, Ddel15 and Ddel32 (Table 1), while evidence of scoring issues or large allele dropouts was not detected. These findings suggest that three of the 12 characterized loci (Ddel6, Ddel15, Ddel32) might be problematic for future population genetic analyses.

The performance of markers Ddel6, Ddel15, and Ddel32 was further assessed using 32 individuals from a separate population (Coorong, South Australia). HWE and F IS estimates for marker Ddel32 were again found to be significantly different from zero (P < 0.001; F IS  = 0.397, P < 0.001), and evidence of null alleles was again recorded (frequency = 0.136). Although HWE and F IS estimates for Ddel6 on this occasion were found to be non-significant following corrections for multiple comparisons (P = 0.069; F IS  = 0.299, P = 0.013), null alleles were again detected (frequency = 0.104) further compromising the reliability of this marker. We recommend that Ddel6 and Ddel32 be excluded from future population genetic studies as these markers are likely to introduce ambiguity. Conversely Ddel15 was found to conform to Hardy–Weinberg expectations (P < 0.340), produced non-significant F IS estimates and null alleles were not detected. Although we cannot explain the discrepancy between the Ddel15 estimates derived from the Venus Bay and Coorong populations, this might be associated with stochastic population processes, however this is most likely associated with differences in null allele frequencies between populations. Therefore we recommend that marker Ddel15 be included but treated with caution in future population genetic studies.

The 10 viable and 9 additional uncharacterized microsatellite markers described in this study provide a valuable resource for future population genetic assessments of D. deltoides in Australia. Estimates of gene flow and population genetic structure using these markers will provide managers with an effective spatial framework for managing the D. deltoides fishery and valuable baseline data for future temporal population monitoring that will assist managers to mitigate the potential for over-exploitation. More importantly this research will promote the future conservation of D. deltoides populations in south eastern Australia.