Skip to main content

Advertisement

Log in

Development and optimisation of molecular assays for microsatellite genotyping and molecular sexing of non-invasive samples of the ghost bat, Macroderma gigas

  • Short Communication
  • Published:
Molecular Biology Reports Aims and scope Submit manuscript

Abstract

The ghost bat (Macroderma gigas) is endemic to Australia but is under threat, with scarce information available on the genetic health of remaining populations. Here, we develop molecular assays for microsatellite genotyping and molecular sexing of non-invasive samples as a genetic monitoring tool to identify individuals, measure genetic diversity and investigate spatial and temporal patterns of habitat use by ghost bats. We identified novel microsatellites through high-throughput sequencing on the Illumina MiSeq platform. Of 48 loci tested, six markers were added to five previously developed microsatellite loci. We developed three Y-linked (DDX3Y, Zfy and SRY) and one X-linked markers (Zfx) to enable molecular identification of sex. To assess performance, all 11 microsatellite and four sex-linked markers were amplified in three multiplex reactions in 160 M. gigas faecal samples from the Pilbara region, Western Australia. The combined markers offered a high level of individual discrimination (PIDsibs = 0.00002) and we detected 19 bats in total (11 males, 4 females and 4 sex undetermined). The number of alleles per locus ranged from 5 to 14 and the average observed and expected heterozygosity across loci were Ho = 0.735 (0.58–0.91) and uHe = 0.785 (0.59–0.89) respectively. Our molecular assays allowed identification of individuals from faecal samples at multiple time points and spatial locations and enabled us to elucidate patterns of habitat usage at the study site. This study highlights the value of our molecular assays as a potential capture-mark-recapture technique for population monitoring for this species.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

References

  1. Richards GC, Hand S, Armstrong KA, Hall LS (2008) Ghost bat Macroderma gigas. In: Van Dyck S, Strahan R (eds) The mammals of Australia, 3rd edn. Chatswood, Reed New Holland, pp 449–450

    Google Scholar 

  2. Woinarski JCZ, Burbidge AA, Harrison PL (2014) The action plan for Australian mammals 2012. CSIRO Publishing, Collingwood

    Book  Google Scholar 

  3. Threatened Species Scientific Committee (2016) Conservation advice Macroderma gigas ghost bat. Department of the Environment and Energy. https://www.environment.gov.au/biodiversity/threatened/species/pubs/174-conservation-advice-05052016.pdf

  4. Hoyle SD, Pople AR, Toop GJ (2001) Mark–recapture may reveal more about ecology than about population trends: demography of a threatened ghost bat (Macroderma gigas) population. Austral Ecol 26(1):80–92. https://doi.org/10.1111/j.1442-9993.2001.01092.pp.x

    Article  Google Scholar 

  5. McKenzie N, Hall L (2008) Macroderma gigas. The IUCN Red List of Threatened Species 2008 e.T12590A3362578

  6. Worthington Wilmer J, Hall L, Barratt E, Moritz C (1999) Genetic structure and male-mediated gene flow in the ghost bat (Macroderma gigas). Evolution 53(5):1582–1591. https://doi.org/10.2307/2640903

    Article  Google Scholar 

  7. Worthington Wilmer J, Moritz C, Hall L, Toop J, Pettigrew JD (1994) Extreme population structuring in the threatened ghost bat, Macroderma gigas: evidence from mitochondrial DNA. Proc R Soc Lond B 257(1349):193–198. https://doi.org/10.1098/rspb.1994.0115

    Article  Google Scholar 

  8. Augusteyn J, Hughes J, Armstrong G, Real K, Pacioni C (2018) Tracking and tracing central Queensland’s Macroderma—determining the size of the Mount Etna ghost bat population and potential threats. Austral Mamm 40(2):243–253

    Article  Google Scholar 

  9. Carroll EL, Bruford MW, DeWoody JA, Leroy G, Strand A, Waits L, Wang J (2018) Genetic and genomic monitoring with minimally invasive sampling methods. Evol Appl 11(7):1094–1119. https://doi.org/10.1111/eva.12600

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Sunnucks P, Hales DF (1996) Numerous transposed sequences of mitochondrial cytochrome oxidase I-II in aphids of the genus Sitobion (Hemiptera: Aphididae). Mol Biol Evol 13(3):510–524. https://doi.org/10.1093/oxfordjournals.molbev.a025612

    Article  CAS  PubMed  Google Scholar 

  11. Meglécz E, Pech N, Gilles A, Dubut V, Hingamp P, Trilles A, Grenier R, Martin J-F (2014) QDD version 3.1: a user-friendly computer program for microsatellite selection and primer design revisited: experimental validation of variables determining genotyping success rate. Mol Ecol Resour 14(6):1302–1313. https://doi.org/10.1111/1755-0998.12271

    Article  CAS  PubMed  Google Scholar 

  12. Delnevo N, Piotti A, van Etten EJ, Stock WD, Byrne M (2019) Isolation, characterization, and cross-amplification of 20 microsatellite markers for Conospermum undulatum (Proteaceae). Appl Plant Sci 7(8):e11283. https://doi.org/10.1002/aps3.11283

    Article  PubMed  PubMed Central  Google Scholar 

  13. Spencer PBS, Tedeschi J (2016) An initial investigation into the genetic diversity, structure and short-range spatial-use by ghost bat in the Hamersley subregion of the Pilbara. Murdoch University, Perth

    Google Scholar 

  14. Andrews S (2014) FastQC: a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/

  15. Chen S, Zhou Y, Chen Y, Gu J (2018) fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34(17):i884–i890. https://doi.org/10.1093/bioinformatics/bty560

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Li H (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:https://arxiv.org/13033997v2 [q-bioGN]

  17. Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, Mesirov JP (2011) Integrative genomics viewer. Nat Biotechnol 29(1):24–26. https://doi.org/10.1038/nbt.1754

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, Rozen SG (2012) Primer3–new capabilities and interfaces. Nucleic Acids Res 40(15):e115. https://doi.org/10.1093/nar/gks596

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Claramunt AMA, White NE, Bunce M, O'Connell M, Bullen RD, Mawson PR (2018) Determination of the diet of the ghost bat (Macroderma gigas) in the Pilbara region of Western Australia from dried prey remains and DNA metabarcoding. Austral J Zool 66(3):195–200

    Article  Google Scholar 

  20. Pompanon F, Bonin A, Bellemain E, Taberlet P (2005) Genotyping errors: causes, consequences and solutions. Nat Rev Genet 6(11):847–859. https://doi.org/10.1038/nrg1707

    Article  CAS  PubMed  Google Scholar 

  21. Broquete T, Petit E (2004) Quantifying genotyping errors in noninvasive population genetics. Mol Ecol 13(11):3601–3608. https://doi.org/10.1111/j.1365-294X.2004.02352.x

    Article  CAS  Google Scholar 

  22. Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28(19):2537–2539. https://doi.org/10.1093/bioinformatics/bts460

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Jones OR, Wang J (2010) COLONY: a program for parentage and sibship inference from multilocus genotype data. Mol Ecol Resour 10(3):551–555. https://doi.org/10.1111/j.1755-0998.2009.02787.x

    Article  PubMed  Google Scholar 

  24. Rousset F (2008) GENEPOP’007: a complete re-implementation of the GENEPOP software for Windows and Linux. Mol Ecol Resour 8(1):103–106. https://doi.org/10.1111/j.1471-8286.2007.01931.x

    Article  PubMed  Google Scholar 

  25. Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes 4(3):535–538. https://doi.org/10.1111/j.1471-8286.2004.00684.x

    Article  CAS  Google Scholar 

Download references

Funding

Development of novel microsatellite markers was undertaken as a research collaboration between Department of Biodiversity, Conservation and Attractions and Biologic Environmental Survey with financial support provided by Biologic Environmental Survey. Whole genome sequencing to identify sex-linked markers was provided by the Oz Mammals Genomics Initiative consortium (https://ozmammalsgenomics.com/consortium/). This Initiative is supported by funding from Bioplatforms Australia through the Australian Government National Collaborative Research Infrastructure Strategy (NCRIS). Bioinformatic analyses were undertaken using resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia.

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization: KO, PBSS; Methodology: KO, RT, SM, PBSS, JT, KA; Formal analysis and investigation: RT, KO; Writing—original draft preparation: RT; Writing—review and editing: KO; Funding acquisition: KO, BD; Resources: CK; Supervision: MB. All authors commented on previous versions of the manuscript and approved the final manuscript.

Corresponding author

Correspondence to Kym Ottewell.

Ethics declarations

Conflict of interest

Author Ottewell is currently receiving ‘fee for service’ research funding for non-invasive genotyping of ghost bats from Biologic Environmental Survey. Author Durrant is Managing Director, and authors Tedeschi and Knuckey are current employees of Biologic Environmental Survey.

Ethical approval

This article does not contain any samples that require handling of the animals by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 57 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ottewell, K., Thavornkanlapachai, R., McArthur, S. et al. Development and optimisation of molecular assays for microsatellite genotyping and molecular sexing of non-invasive samples of the ghost bat, Macroderma gigas. Mol Biol Rep 47, 5635–5641 (2020). https://doi.org/10.1007/s11033-020-05544-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11033-020-05544-x

Keywords

Navigation