Genetic diversity of the Green Turtle (Testudines: Cheloniidae: Chelonia mydas (Linnaeus, 1758)) population nesting at Kosgoda Rookery, Sri Lanka

 

Article

INTRODUCTION

Conservation efforts are directed at preserving the existing genetic variation within endangered species (Lynch 1996). There are three basic measures of genetic variation: heterozygosity within individual genomes, allelic variation among individuals within a population, and allele frequency differences among populations. Genetic variation plays a critical role in the ability of populations to respond to changing environments, and thus it likely plays an important role in the survival and adaptability of species.

The Green Turtle Chelonia mydas is listed as Endangered in the IUCN Red List of Threatened Animals (Seminoff 2004). Knowledge of population structure is important in defining conservation priorities for this endangered species. Mitochondrial DNA (mtDNA) is often used to assess genetic diversity, population structure and migration patterns of sea turtles (Bjorndal et al. 2005; Formia et al. 2006; Naro-Maciel et al. 2007; Bowen & Karl 2007; LeRoux et al. 2012). Microsatellite loci have also been used to assess genetic variation within populations (FitzSimmons et al. 1995; Gullberg et al. 1997; Lee 2008) and among populations (DeWoody & Avise 2000). Microsatellite markers are ideal for studies of genetic diversity because of their high frequency in the genome, high polymorphism, co-dominance and the cost-effectiveness of analysis. Because microsatellites are bi-parentally inherited they can reveal patterns of sex-biased gene flow, especially when used in combination with mtDNA (FitzSimmons et al. 1997). Even DNA from degraded or poorly preserved specimens may contain a sufficient number of copies of a short microsatellite target sequence suitable for amplification (Queller et al. 1993). The earliest sea turtle microsatellite markers developed by FitzSimmons et al. (1995) and FitzSimmons (1998) are particularly well-used, with some loci having been applied to almost all of the sea turtle species studied using microsatellites (Lee 2008). Many new microsatellite markers have been developed for Green Turtles recently (Dutton & Frey 2009; Frey et al. 2013). Most of the studies use both mtDNA and microsatellite analysis to improve knowledge of the genetic diversity of sea turtles (Naro-Maciel et al. 2008; Yilmaz et al. 2011; Bagda et al. 2012).

Genetic studies have not been conducted for any of the five species of sea turtles nesting in Sri Lanka, although many studies have examined genetic structure of Green Turtles in the Indian Ocean (Karl et al. 1992; Dethmers et al. 2006; Bourjea et al. 2007; Dethmers & Baxter 2010). Dethmers et al. (2006) examined the mtDNA variation across 27 Green Turtle rookeries in Australasia and recorded 17 genetically distinct breeding stocks, regarded as management units, consisting either of individual rookeries or groups of rookeries separated by more than 500km. Bourjea et al. (2007) used mtDNA variation to analyze the genetic structure of southwestern Indian Ocean Green Turtle populations and detected gene flow from the Atlantic Ocean into the Indian Ocean via the Cape of Good Hope. The current study investigated the genetic diversity of the Green Turtle population nesting at Kosgoda Rookery using microsatellites. Although more than 90% of the nests recorded at Kosgoda Rookery are of Green Turtle (Ekanayake et al. 2008) its population is small compared to other rookeries in the world with a mean of 298.4 annual nests (Ekanayake et al. 2010), among which about half of the hatchlings show multiple paternity (Ekanayake et al. 2013a). Assessing the genetic diversity among and within individuals of this nesting population will provide important information for defining conservation priorities for this endangered species. This information is important in developing global, multi-scale regional management units as highlighted in Wallace et al. (2010) describing the complexities in sea turtle population structures and thus providing a flexible, dynamic framework for evaluating threats, identifying high diversity areas and most importantly bridging data gaps.

Kosgoda Turtle Rookery (6.550000 N & 80.033333 E) on the southwestern coast of Sri Lanka (Fig. 1) hosts the second largest Green Turtle rookery in Sri Lanka, with year-round nesting. We collected tissue samples from nesting females after they had completed the nesting process. A small area in one of the front flippers was cleaned with 95% ethanol and a skin scraping (~0.5cm2) was taken using a sterile surgical blade. Samples were stored in 1.5ml centrifuge tubes containing 95% ethanol. The tag number was noted if the female had already been tagged, or a new tag was placed to ensure the same female was not sampled multiple times. Permission to collect tissue samples was obtained from the Department of Wildlife Conservation (DWC), Sri Lanka.

Extraction of genomic DNA and amplification of microsatellites

Approximately 0.1g of skin tissue from each sample was placed on a glass plate and cut into small pieces using a sterile surgical blade. The tissue was placed in 500μl of a 5% aqueous suspension of Chelex® (BioRad, Richmond, U.S.A.) in 1.5ml Eppendorf tube and incubated for three hours at 550C with continuous shaking. Samples were vortexed for 30s and placed in a heating block at 950C for five minutes. After centrifugation at 11,600g (RCF) for five minutes, the supernatant containing extracted DNA was removed and stored at -200C.

Six di-nucleotide microsatellite loci, Cm3, Cm58, Cm72, Cm84, Cc117 (FitzSimmons et al. 1995) and Cc7 (FitzSimmons 1998), were amplified by polymerase chain reaction (PCR). Four of these loci (Cm3, Cm58, Cm72, and Cm84) were developed from Green Turtle DNA (FitzSimmons et al. 1995) and the remaining two (Cc117, Cc7) were from Loggerhead Turtle DNA (FitzSimmons et al. 1995; FitzSimmons 1998). The PCR mixture contained 3µl of template DNA, 0.5 µM of each primer (forward and reverse), 0.2mM dNTPs, 0.5 unit of Taq DNA polymerase, 1.5 µl of 10 x PCR buffer with MgCl2 in a final volume of 10 µl. The amplification procedure consisted of initial denaturation at 950C for 2 min followed by 35 cycles with primer annealing at 550C for Cc7, 620C for Cm3, Cm58, Cm72 and Cc117, or 640C for Cm84 for 1 min, strand synthesis at 720C for 1 min and denaturation at 950C for 45 sec, followed by final extension at 720C for 7 min.

Polyacrylamide gel electrophoresis

Amplified products were separated by electrophoresis on a 6% denaturing polyacrylamide gel. To denature the samples, 1.5µl of loading buffer was mixed with 10µl of PCR product and incubated at 950C for five minutes. Tubes were then immediately placed on ice for rapid cooling. The samples were loaded and the gel (48cm) was run at 1,400v for three hours at 45–50 0C using a 20bp DNA ladder as a size standard (Bangalore Genei, Pvt Ltd. India). Gels were fixed with 100ml of 5% acetic acid for 20 minutes with gentle shaking, washed twice with distilled water and stained with 0.4% silver nitrate for 20 minutes, then washed in distilled water. DNA bands were visualized after developing the gels in 3% NaOH. A solution of 5% acetic acid was added to prevent further development. The size of the DNA bands was determined using a gel documentation system (Bio-Vision, Germany, 3000-WL/26M) and Vision-Capt (Version 14.3) software. Alleles of approximately one bp difference in size were scored to the nearest even number.

 

 

Data analysis

The number of alleles for each locus was determined, and the mean number of alleles per locus and the allele frequencies for each locus were calculated. Deviations from Hardy-Weinberg equilibrium (HWE) and heterozygosity (observed and expected) for each locus were calculated by the Markov chain method, following the exact test of Guo & Thompson (1992) with 1,000,000 forecasted chain length and 100,000 dememorization steps using Arlequin software (Version 3.1, Excoffier, Laval, Schneider, 2005). Estimates of within population structure (fixation indices, FIS), analysis of molecular variance (AMOVA, Excoffier et al. 1992), measures of genetic diversity, and tests of pairwise linkage disequilibrium (Slatkin & Excoffier 1996) were also calculated using Arlequin software. The microsatellite data were analysed using the software Micro-Checker (Version 2.2.3) to check for null alleles (van Oosterhout et al. 2004).

 

 

 

 

RESULTS

 

All microsatellite loci were highly polymorphic (Table 1). A total of 149 alleles were observed for the six microsatellites (Fig. 2). The most polymorphic locus was Cm72, with 40 alleles, and the least polymorphic was Cm58, with 15 alleles (Fig. 2). The mean number of alleles per locus was 24.7. Although there were predominant alleles observed in all loci, no allele had a frequency of greater than 20%. A high proportion of uncommon (< 5% occurrence) alleles were observed at each locus, ranging from 46.7% (at Cm58) to 85.0% (at Cm72) of alleles (Fig. 2). The mean observed and expected heterozygosity across all loci was 0.75 (range 0.66–0.86) and 0.93 (range 0.89–0.97), respectively (Table 1). The Arlequin analysis indicated all loci (exact test, p<0.05) displayed deviations from HWE due to a deficit of heterozygous individuals. The mean FIS over all loci was 0.2; the FIS for each locus is given in Table 1. The positive FIS values indicate that there are fewer heterozygous individuals than would be expected for all the loci, suggesting possible inbreeding. Hence, about 20% of the population would be expected to have both alleles derived from inbreeding at one locus at least; however, the observed heterozygosity may be an underestimation due to the presence of null alleles. Therefore, data were analyzed using Micro-Checker software that detects the occurrence of null alleles. All loci, except Cm58, have null alleles (Table 2) therefore this population is possibly in HWE.

The mean gene diversity, as estimated by Arlequin, over all six loci was 0.65 (± 0.36, SD). The nested analysis of molecular variance (AMOVA) was calculated (average over 6 loci) and genetic variation of 19.3% among individuals (variance components, 0.54) and 80.7% within individuals (variance components, 2.26) were observed. Pairwise significance linkage disequilibrium (p<0.05) was observed for many pairs of loci (Table 3).

 

 

 

DISCUSSION

 

A high genetic diversity within and among individuals of the Green Turtle population nesting at Kosgoda Rookery was observed. All six microsatellite loci tested were highly polymorphic in the Kosgoda Green Turtle population. According to population genetics theory, a locus is considered polymorphic when the frequency of the most common allele is equal to, or less than, 0.99 (Ahmad et al. 1977). The mean number of alleles per locus, and the number of alleles per locus, was higher in Green Turtles nesting at Kosgoda when compared with other nesting Green Turtle populations (Table 4). A population with high genetic diversity has a greater chance of possessing at least some individuals with a genetic constitution that enables survival if new pressures, such as environmental disasters, occur. If genetic diversity is very low, the population is at risk as none of the individuals may possess characteristics that allow adaptation to the new environmental conditions. Such a population could suddenly disappear. Although the nesting Green Turtle population at Kosgoda is not very large, viability of the population in the next few generations is unlikely to be substantially reduced by genetic factors.

Genetic diversity in a population can also be estimated by the mean levels of heterozygosity in addition to the mean number of alleles per locus, or the percentage of polymorphic loci. Heterozygosity is an important measurement of population diversity at the genetic level, and has drawn much attention from ecologists and aquaculturists (Xu et al. 2001). Conservation of genetic variability is important to the overall viability of populations, because decreased genetic variability leads to increased levels of inbreeding and reduced fitness (Young et al. 1996; Frankham 2006). When analyzed using Arlequin software, heterozygosity deficiency was observed in the Kosgoda Green Turtle population; however, the mean Ho was within the range of many other Green Turtle populations in the world (Table 4). High levels of heterozygosity in Green Turtle populations has been reported in several studies using microsatellite markers (FitzSimmons et al. 1995; Rico et al. 1996; Norris et al. 1999). The Ho was higher than the He in most of the population studies by Roberts et al. (2004). The Ho of the Kosgoda population of nesting Green Turtles was less than He for all six loci based on tests for HWE. Hence, the Kosgoda Green Turtle population appears to deviate from HWE. Deviation from HWE at some microsatellite loci was reported by Roberts et al. (2004) in studies on Green Turtle populations. Reduced heterozygosity is one of the factors contributing to departure from HWE, and may result from one or more factors including the presence of null alleles (Callen et al. 1993; Pemberton et al. 1995; Roberts et al. 2004), small sample size and inbreeding (Kumar et al. 2006). Null alleles might occur at microsatellite loci, resulting in weak or absent bands after amplification (O’Connell & Wright 1997), a disadvantage of microsatellite markers. According to Shriver et al. (1995) the problem of small sample size can be overcome by using multiple independent loci to increase resolution and statistical confidence. Even with arguments for larger sample sizes, a sample size of 15 per location has been determined as sufficient for genetic analyses of sea turtles (including microsatellite surveys; Roberts et al. 2004). In the current study, 68 samples were analysed with six microsatellite loci; therefore, the sample size can be considered sufficient to resolve the genetic diversity of the nesting Green Turtle population at Kosgoda.

Although the mean FIS value indicated the heterozygote deficiency, the results of analysis by Micro-Checker, which takes into account the presence of null alleles, suggested that the Kosgoda nesting Green Turtle population was possibly in HWE. Hence, heterozygosity deficiency could be due to the presence of null alleles. The stutter bands cause problems in differentiation between homozygous and heterozygous individuals (Luty et al. 1990; DeWoody et al. 2006) and can contribute to high heterozygosity. The Micro-Checker result, however, suggested stuttering might have resulted in scoring errors only in locus Cc117 (Table 2). Therefore, the contribution of stutters to heterozygosity observed in the present study may be negligible; however, gene flow from outside populations would be beneficial to avoid inbreeding and the future erosion of genetic diversity.

The nest paternity of the Green Turtle population at Kosgoda was analyzed using the same microsatellites for the six loci and reported evidence of multiple paternity with 47% of the clutches are being sired by two (62.5%) or three (37.5%) males (Ekanayake et al. 2013a). Further, the results of successive clutch analysis show that the dominant father sired 50.0% of the total offspring followed by 33.3% by the second male. Moreover, the study also reported the same paternal alleles at all six loci in successive clutches, suggesting that the male or males that sired the first clutch also sired the others for a given female. This provides evidence for multiple mating with the same male during a nesting season and/or sperm storage. In populations where multiple matings occur, knowledge of its prevalence and effects on paternity distribution within a natural assemblage is critical to comprehend population structure. This information can therefore be of great importance to the management and conservation of threatened species such as sea turtles.

Rekawa and Kosgoda have been identified as major Green Turtle nesting rookeries in Sri Lanka, and many minor rookeries have also been identified (Amarasooriya 2000). Green Turtles from Rekawa and Kosgoda rookeries have migrated thousands of kilometers across the seas to the east and west of Sri Lanka (Ekanayake et al. 2013b; Richardson et al. 2013). Although the Green Turtles nesting at these rookeries may be different populations, there is a high possibility of mixing within these populations or with nesting populations from other countries while at the feeding grounds or along migratory pathways. The Green Turtle population that nests at the Kosgoda Rookery of Sri Lanka has high genetic diversity. Despite its small size, its high genetic diversity will be vital to carry all the healthy useful characters to the progeny. If the Kosgoda Green Turtle population is declining, it is not due to genetic factors and hence, necessary actions should be taken to reduce natural and anthropogenic threats to the population. Although it was initially planned to carry out the study using all the major Green Turtle nesting sites in Sri Lanka, the permits (from the Department of Wildlife Conservation, Sri Lanka) were issued to collect samples only from the Kosgoda Rookery. Further genetic sampling and analysis is needed from other Green Turtle rookeries in the country to further understand the genetic diversity of the Green Turtles in Sri Lanka and determine if they are of different genetic stock.

The sea turtle population nesting at Rekawa has declined by ~60% during the last decade (Ekanayake et al. 2012). Although a similar decline was not observed in the Green Turtle population nesting at the Kosgoda Rookery (Ekanayake et al. 2010), nesting population is small and they face many threats. Information about population structure is important in defining conservation priorities for this species, as the viability of a population is unlikely to be reduced if high genetic diversity is maintained.

 

 

References

 

 

Ahmad, M., D.O.F. Skibinski & J.A. Beardmore (1977). An estimate of the amount of genetic variation in the common mussel Mytilus edulis. Biochemical Genetics 15(9/10): 833–846.

Amarasooriya, K.D. (2000). Classification of marine turtle nesting beaches of southern Sri Lanka, pp. 228–237. In: Pilchar, N. & G. Ismail (eds.). Sea Turtles of The Indo Pacific, Research, Management and Conservation. Proceedings of the second ASEAN Symposium and workshop on sea turtle biology and conservation.

Bagda, E., F. Bardakci & O. Turkozan (2012). Lower genetic structuring in mitochondrial DNA than nuclear DNA among the nesting colonies of Green Turtles (Chelonia mydas) in the Mediterranean. Biochemical Systematics and Ecology 43: 192–199.

Bjorndal, K.A., A.B. Bolten & S. Troeng (2005). Population structure and genetic diversity in green turtles nesting at Tortuguero, Costa Rica, based on mitochondrial DNA control region sequences. Marine Biology 147: 1449–1457.

Bourjea, J., S. Lapegue, L. Gagnevin, D. Broderick, J.A. Mortimer, S. Ciccione, D. Roos, C. Taquet & H. Grizel (2007). Phylogeography of the green turtle, Chelonia mydas in the southwest Indian Ocean. Molecular Ecology 16: 175–186.

Bowen, B.W. & S.A. Karl (2007). Population genetics and phylogeography of sea turtles. Molecular Ecology 16: 4886–4907.

Callen, D.F., A.D. Thompson, Y. Shen, H.A. Phillips, R.I. Richards, J.C. Mulley & G.R. Sutherland (1993). Incidence and origin of “null” alleles in the (AC)n microsatellite markers. American Journal of Human Genetics 52: 922–927.

Dethmers, K.E.M. & P.W.J. Baxter (2010). Extinction risk analysis of exploited Green Turtle stocks in the Indo-Pacific. Animal Conservation 14: 140–150.

Dethmers, K.E.M., D. Broderick, C. Moritz, N.N. FitzSimmons, C.J. Limpus, S. Lavery, S. Whiting, S., Guinea, R.I.T Prince & R. Kennett (2006). The genetic structure of Australasian Green Turtles (Chelonia mydas): exploring the geographical scale of genetic exchange. Molecular Ecology 15: 3931–3946.

DeWoody, J., J.D. Nason & V.D. Hipkins (2006). Mitigating scoring errors in microsatellite data from wild populations. Molecular Ecology Notes 6: 951–957.

DeWoody, J.A. & J.C. Avise (2000). Microsatellite variation in marine, freshwater and anadromous fishes compared with other animals. Journal of Fish Biology 56: 461–473; http://doi.org/10.1371/journal.pone.0015465

Dutton, P.H. & A. Frey (2009). Characterization of polymorphic microsatellite markers for the green turtle (Chelonia mydas). Molecular Ecology Resources 9: 354–356.

Ekanayake, E.M.L., R.S. Rajakaruna, T. Kapurusinghe, M.M. Saman, P. Samaraweera & K.B. Ranawana (2012). A declining trend of nesting frequency of sea turtles at the largest rookery in Sri Lanka. T.T. Jones and B.P. Wallace (Comp.). Proceedings of the thirty-first Annual Symposium on Sea Turtle Biology and Conservation. NOAA Technical Memorandum, NOAA NMFS-SEFSC-631, 201.

Ekanayake, E.M.L., T. Kapurusinghe, M.M. Saman, & A.M.D.S. Rathnakumara (2013b). Re-nesting movements and post-nesting migrations of green turtles tagged in two turtle rookeries in Sri Lanka. J. Blumenthal, A. Panagopoulou, and A.F. Rees, Compilers. Proceedings of the Thirtieth Annual Symposium on Sea Turtle Biology and Conservation. NOAA Technical Memorandum NMFS-SEFSC-640, 123.

Ekanayake, E.M.L., T. Kapurusinghe, M.M. Saman, P. Samaraweera, K.B. Ranawana, & R.S. Rajakaruna (2010). Nesting Behaviour of the Green Turtle at Kosgoda Rookery, Sri Lanka. Ceylon Journal of Science (Biological Science) 39(2): 109–120.

Ekanayake, E.M.L., T. Kapurusinghe, M.M. Saman, P. Samaraweera, K.B. Ranawana and R.S. Rajakaruna (2013a). Paternity of Green Turtles (Chelonia mydas) clutches laid at Kosgoda, Sri Lanka. Herpetological Conservation and Biology 8: 27–36.

Ekanayake, E.M.L., T. Kapurusinghe, M.M. Saman, R.S. Rajakaruna, P. Samaraweera & K.B. Ranawana (2008). Nesting Frequency of Marine Turtles Visiting on Kosgoda Beach, Southern Sri Lanka. Rees, A.F., M. Frich, A. Panagopoulou & K. Williams (Comp.). Proceedings of the Twenty-Seventh Annual Symposium on Sea Turtle Biology and Conservation, South Carolina, USA. NOAA Technical Memorandum NMFS-SEFSC-569: 232–233.

Excoffier, L., G. Laval & S. Schneider (2005). Arlequin, version 3.1: an integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online 1: 47–50.

Excoffier, L., P. Smouse & J. Quattro (1992). Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131: 479–491.

FitzSimmons, N.N. (1998). Single paternity of clutches and sperm storage in the promiscuous green turtle (Chelonia mydas). Molecular Ecology 7: 575–584.

FitzSimmons, N.N., C. Moritz & S.S. Moor (1995). Conservation and dynamics of microsatellite loci over 300 million years of marine turtle evolution. Molecular Biology and Evolution 12(3): 432–440.

FitzSimmons, N.N., C. Moritz, C.L. Limpus, L. Pope & R. Prince (1997). Geographic structure of mitochondrial and nuclear gene polymorphisms in Australian Green Turtle populations and male-biased gene flow. Genetics 147: 1843–1854.

Formia, A., B.J. Godley, J.F. Dontaine & M.W. Bruford (2006). Mitochondrial DNA diversity and phylogeography of endangered Green Turtle (Chelonia mydas) populations in Africa. Conservation Genetics 7: 353–369.

Frankham, R. (2006). Genetics and Extinction. Biological Conservation 126: 131–140.

Frey, A., P.H. Dutton & G.H. Balazs (2013). Insights on the demography of cryptic nesting by green turtles (Chelonia mydas) in the main Hawaiian Islands from genetic relatedness analysis. Journal of Experimental Marine Biology and Ecology 442: 80–87.

Gullberg, A., H. Tegelstrom & M. Olsson (1997). Microsatellites in the Sand Lizard (Lacerta agilis): description, variation, inheritance, and applicability. Biochemical Genetics 35: 281–295.

Guo, S.W. & E.A. Thompson (1992). Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics 48(2): 361–372.

Karl, S.A., B.W. Bowen & J.C. Avise (1992). Global population genetic structure and male-mediated gene flow in the green turtle (Chelonia mydas): RFLP analyses of anonymous nuclear loci. Genetics 131: 163–173.

Kumar, S., J. Gupta, N. Kumar, K. Dikshit, N. Navani, P. Jain & M. Nagarajan (2006). Genetic variation and relationships among eight Indian Riverine Buffalo breeds. Molecular Ecology 15: 593–600.

Lee, P.L.M. (2008). Molecular ecology of marine turtles: New approaches and future directions. Journal of Experimental Marine Biology and Ecology 356: 25–42.

LeRoux, R.A., P.H. Dutton, F.A. Abreu-Grobois, C.J. Lagueux, C.L. Campbell, E. Delcroix, E. Chevalier, J.A. Horrocks, Z. Hillis0starr, S. Troeng, E. Harrison & S. Stapleton (2012). Re-examination of population structure and phylogeography of Hawksbill Turtles in the wider Caribbean using longer mtDNA sequences. Journal of Heredity 103(6): 806–820.

Luty, J.A., Z. Guo, H.F. Willard, D.H. Ledbetter, S. Ledbetter & M. Litt (1990). Five polymorphic microsatellite VNTRs on the human X chromosome. American Journal of Human Genetics 46(4): 776–783.

Lynch, M. (1996). A quantitative-genetic perspective on conservation issues, pp. 471–501. In: Avise, J.C. & J.L. Hamrick (eds.). Conservation Genetics: Case Studies from Nature. Chapman and Hall, New York.

Naro-Maciel, E., J.H. Becker, E.H.S.M. Lima, M.A. Marcovaldi & R. Desalle (2007). Testing dispersal hypotheses in foraging green Sea Turtles (Chelonia mydas) of Brazil. Journal of Heredity 98(1): 29–39.

Naro-Maciel, E., M. Le, N.N. FitzSimmons & G. Amato (2008). Evolutionary relationships of marine turtles: a molecular phylogeny based on nuclear and mitochondrial genes. Molecular Phylogenetics and Evolution 49: 659–662.

Norris, A.T., D.G. Bradley & E.P. Cunningham (1999). Microsatellite genetic variation between and within farmed and wild Atlantic Salmon (Salmo salar) populations. Aquaculture 180: 247–264.

O’Connell, M. & J.M. Wright (1997). Microsatellite DNA in fishes. Biomedical and Life Sciences 7(3): 331–363.

Pemberton, J.M., J. Slate, D.R. Bancroftt & J.A. Barrett (1995). Nonamplifying alleles at microsatellite a caution for parentage and population loci: studies. Molecular Ecology 4: 249–252.

Queller, D.C., J.E. Strassmann & C.R. Hughes (1993). Microsatellites and kinship. Trends in Ecology and Evolution 8: 285–288.

Richardson, P.B., A.C. Broderick, M.S. Coyne, L. Ekanayake, T. Kapurusinghe, C. Premakumara, S. Ranger, M.M. Saman, M.J. Witt & B.J. Godley (2013). Satellite telemetry reveals behavioural plasticity in a Green Turtle population nesting in Sri Lanka. Marine Biology 160(6): 1415–1426.

Rico, C., I. Rico & G. Hewitt (1996). 470 Million years of conservation of microsatellite loci among fish species. Proceedings of the Royal Society Biological Sciences 263(1370): 549–557; http://doi.org/10.1098/rspb.1996.0083

Roberts, M.A., T.S. Schwartz & S.A. Karl (2004). Global population genetic structure and male-mediated gene flow in the Green Sea Turtle (Chelonia mydas): analysis of microsatellite loci. Genetics 166: 1857–1870.

Seminoff, J.A. (2004). Guest editorial: Sea Turtles, Red Listing, and the need for regional assessments. Marine Turtle Newsletter 106: 4–6.

Shriver, M.D., L. Jin, E. Boerwinkle, R. Deka, R.E. Ferrell & R. Chakraborty (1995). A novel measure of genetic distance for highly polymorphic tandem repeat loci. Molecular Biology and Evolution 12(5): 914–920.

Slatkin, M. & L. Excoffier (1996) Testing for linkage disequilibrium in genotypic data using the EM algorithm. Heredity 76: 377–383.

van Oosterhout, C., W.F. Hutchinson, D.P.M. Wills & P. Shipley (2004). MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology Notes 4: 535–538.

Wallace, B.P., A.D. DiMatteo, B.J. Hurley, E.M. Finkbeiner, A.B. Bolten, M.Y. Chaloupka, B.J. Hutchinson, F.A. Abreu-Grobois, D. Amorocho, K.A. Bjorndal, J. Bourjea, B.W. Bowen, R.B. Dueñas, P. Casale, B.C. Choudhury, A. Costa, P.H. Dutton, A. Fallabrino, A. Girard, M. Girondot, M.H. Godfrey, M. Hamann, M. López-Mendilaharsu, M.A. Marcovaldi, J.A. Mortimer, J.A. Musick, R. Nel, N.J. Pilcher, J.A. Seminoff, S. Troëng, B. Witherington & R.B. Mast (2010). Regional Management Units for marine turtles: A novel framework for prioritizing conservation and research across multiple scales. PLoS ONE 5(12): e15465; http://doi.org/10.1371/journal.pone.0015465

Xu, Z., J.H. Primavera, L.D. De la Pena, P. Pettil, J. Belak & A. Alcivar-Waren (2001). Genetic diversity of wild and cultured Black Tiger Shrimp Penaeus monodon in the Philippines using microsatellites. Aquaculture 199: 13–40.

Yilmaz, C., O. Turkozan & F. Bardakci (2011). Genetic structure of Loggerhead Turtle (Caretta caretta) populations in Turkey. Biochemical Systematics and Ecology 39: 266–276.

Young, A., T. Boyle & T. Brown (1996). The population genetic consequences of habitat fragmentation for plants. Trends in Ecology & Evolution 11(10): 413–418.