Mammalian Biology

, Volume 77, Issue 1, pp 67–70 | Cite as

Low genotyping error rates in non-invasively collected samples from roe deer of the Bavarian Forest National Park

  • Joerns FickelEmail author
  • Oleg A. Bubliy
  • Julia Brand
  • Kathrin Mayer
  • Marco Heurich
Short Communication


Genetic wildlife monitoring is increasingly carried out on the basis of non-invasively collected samples, whereby the most commonly used DNA sources are skin appendages (hairs, feathers) and faeces. In order to guide decisions regarding future adequate ways to monitor the roe deer (Capreolus capreolus) population of the Bavarian Forest National Park in Germany, we tested these two different types of DNA source materials to compare their suitability for genetic monitoring. We determined the haplotypes (d-loop) of 19 roe deer and genotyped each individual (tissue, hairs, faeces) across 12 microsatellite loci. The amount of missing and erroneous microsatellite alleles obtained from hair and faeces samples, respectively, was estimated based on comparisons with the corresponding tissue sample control. We observed no missing alleles in hair samples, but in fecal samples PCR failed in 30 out of 228 instances (19 individuals x 12 loci), corresponding to a frequency of missing alleles of 13.2% across all loci and individuals. In genotypes generated from hairs erroneous alleles were detected in 2 out of 228 instances (0.9%), while genotypes retrieved from fecal samples displayed erroneous alleles in 6 out of 198 remaining instances (3%). We conclude that both hair and fecal samples are generally well suited for genetic roe deer monitoring, but that fecal sample based analyses require a larger sample size to account for higher PCR failure rates.


Capreolus capreolus Hair Faeces d-Loop Microsatellite genotypes 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Beja-Pereira, A., Oliveira, R., Alves, P.C., Schwartz, M.K., 2009. Advancing ecological understandings through technological transformations in noninvasive genetics. Mol. Ecol. Resources 9, 1279–1301.CrossRefGoogle Scholar
  2. Bishop, M.D., Kappes, S.M., Keele, J.W., Stone, R.T., Sunden, S.L.F., Hawkins, G.A., Solinas Toldo, S., Fries, R., Grosz, M.D., Yoo, J., Beattie, C.W., 1994. A genetic linkage map for cattle. Genetics 136, 619–639.PubMedPubMedCentralGoogle Scholar
  3. Clauss, M., Lason, K., Gehrke, J., Lechner-Doll, M., Fickel, J., Grune, T., Streich, W.J., 2003. Captive roe deer (Capreolus capreolus) select for low amounts of tannic acid but not quebracho: fluctuation of preferences and potential benefits Comp. Biochem. Physiol. B 136, 369–382.CrossRefGoogle Scholar
  4. Creel, S., Spong, G., Sands, J.L., Rotella, J., Zeigle, J., Joe, L., Murphy, K.M., Smith, D., 2003. Population size estimates in Yellowstone wolves with error-prone nonin-vasive microsatellite genotypes. Mol. Ecol. 12, 2003–2009.CrossRefGoogle Scholar
  5. Constable, J.J., Packer, C., Collins, D.A., Pusey, A.E., 1995. Nuclear DNA from primate dung. Nature 373, 393.CrossRefGoogle Scholar
  6. Davison, A., Birks, J.D.S., Brookes, R.C., Braithwaite, T.C., Messenger, J.E., 2002. On the origin of faeces: morphological versus molecular methods for surveying rare carnicores from their scats. J. Zool. 257, 141–143.CrossRefGoogle Scholar
  7. Fickel, J., Hohmann, U., 2006. Amethodological approach for non-invasive sampling for population size estimates in wild boars (Sus scrofa). Eur. J. Wildl. Res. 52, 28–33.CrossRefGoogle Scholar
  8. Fickel, J., Pitra, C., Joest, B.A., Hofmann, R.R., 1998. A novel method to evaluate the relative tannin-binding capacities of salivary proteins. Comp. Biochem. Physiol. C 122, 225–229.Google Scholar
  9. Fickel, J., Reinsch, A., 2000. Microsatellite markers for the European Roe deer (Capre-olus capreolus). Mol. Ecol. 9, 994–995.CrossRefGoogle Scholar
  10. Fickel, J., Wagener, A., Ludwig, A., 2007. Semen cryopreservation and the conservation of endangered species. Eur. J. Wildl. Res. 53, 81–89.CrossRefGoogle Scholar
  11. Gagneux, P., Boesch, C., Woodruff, D.S., 1997. Microsatellite scoring errors associated with noninvasive genotyping based on nuclear DNA amplified from shed hair. Mol. Ecol. 6, 861–868.CrossRefGoogle Scholar
  12. Gaillard, J.M. 1988. Contribution a la dynamique des populations de grands mammifères. L’exemple du chevreuil (Capreolus capreolus). PhD thesis, University of Lyon, Lyon, France.Google Scholar
  13. Goossens, B., Waits, L.P., Taberlet, P., 1998. Plucked hair samples as a source of DNA: reliability of dinucleotide microsatellite genotyping. Mol. Ecol. 7, 1237– 1241.CrossRefGoogle Scholar
  14. Kalinowski, S.T., Taper, M.L., 2006. Maximum likelihood estimationof the frequency of null alleles at microsatellite loci. Cons. Genet. 7, 991–995.CrossRefGoogle Scholar
  15. Kalz, B., Jewgenow, K., Fickel, J., 2006. Structure of an otter population in Germany – results of DNA and hormone analyses from faecal samples. Mamm. Biol 71, 321–335.CrossRefGoogle Scholar
  16. Larkin, M.A., Blackshields, G., Brown, N.P., Chenna, R., McGettigan, P.A., McWilliam, H., Valentin, F., Wallace, I.M., Wilm, A., Lopez, R., Thompson, J.D., Gibson, T.J., Higgins, D.G., 2007. CLUSTAL W and CLUSTAL X version 2.0. Bioinformatics 23, 2947–2948.CrossRefGoogle Scholar
  17. Marshall, T.C., Slate, J., Kruuk, L.E.B., Pemberton, J.M., 1998. Statistical confidence for likelihood-based paternity inference in natural populations. Mol. Ecol. 7, 639–655.CrossRefGoogle Scholar
  18. Miller, C.R., Joyce, P., Waits, L.P., 2002. Assessing allelic dropout and genotype reliability using maximum likelihood. Genetics 160, 357–366.PubMedPubMedCentralGoogle Scholar
  19. Partl, E., Szinovatz, V., Reimoser, F., Schweiger-Adler, J., 2002. Forest restoration and browsing impact by roe deer. Forest Ecol. Manage. 159, 87–100.CrossRefGoogle Scholar
  20. Pielowski, Z., 1984. Some aspects of population structure and longevity of field roe deer. Acta Theriologica 29, 17–33.CrossRefGoogle Scholar
  21. Rice, W.R., 1989. Analyzing tables of statistical tests. Evolution 43, 223–225.CrossRefGoogle Scholar
  22. Røed, K.H., Midthjell, L., 1998. Microsatellites in reindeer, Rangifer tarandus, and their use in other cervids. Mol. Ecol. 7, 1773–1776.CrossRefGoogle Scholar
  23. Rousset, F., 2008. GENEPOP’007: a complete re-implementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resources 8, 103–106.CrossRefGoogle Scholar
  24. Ruibal, M., Peakall, R., Claridge, A., Murray, A., Firestone, K., 2010. Advancement to hair-sampling surveys of a medium-sized mammal: DNA-based individual identification and population estimation of a rare Australian marsupial, the spotted-tailed quoll (Dasyurus maculatus). Wildl. Res. 37, 27–38.CrossRefGoogle Scholar
  25. Sage, R.B., Hollins, K., Gregory, C.L., Woodburn, M.I.A., Carroll, J.P., 2004. Impact of roe deer Capreolus capreolus browsing on understorey vegetation in small farm woodlands. Wildl. Biol. 10, 115–120.CrossRefGoogle Scholar
  26. Strandgaard, H., 1972. The roe deer (Capreolus capreolus) population at Kalø and the factors regulating its size. Danish Rev. Game Biol. 7, 1–205.Google Scholar
  27. Tamura, K., Dudley, J., Nei, M., Kumar, S., 2007. MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Mol. Biol. Evol. 24, 1596–1599.CrossRefGoogle Scholar
  28. Thieven, U., Harlizius, B., Simon, D., 1995. Dinucleotide repeat polymorphism at the bovine HAUT1 and HAUT14 loci. Anim. Genet. 26, 123.CrossRefGoogle Scholar
  29. Thompson, J.D., Gibson, T.J., Plewniak, F., Jeanmougin, F., Higgins, D.G., 1997. The CLUSTALX windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucl. Acids Res. 24, 4876–4882.CrossRefGoogle Scholar
  30. van Ooosterhout, C., Hutchinson, W.F., Wills, D.P.M., Shipley, P., 2004. Micro-Checker: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538.CrossRefGoogle Scholar
  31. Vernesi, C., Pecchioli, E., Caramelli, D., Tiedemann, R., Randi, E., Bertorelle, G., 2002. The genetic structure of natural and reintroduced roe deer (Capreolus capreolus) populations in the Alps and central Italy, with reference to the mitochondrial DNA phylogeography of Europe. Mol. Ecol. 11, 1285–1297.CrossRefGoogle Scholar
  32. Wiehler, J., Tiedemann, R., 1998. Phylogeography of the European roe deer Capreolus capreolus as revealed by sequence analysis of the mitochondrial control region. Acta Theriol. Suppl. 5, 187–197.CrossRefGoogle Scholar
  33. Wotschikowsky, U., 2010. Ungulates and their management in Germany. In: Apollonio, M., Andersen, R., Putman, R. (Eds.), European Ungulates and their Management in the 21st Century. Cambridge University Press, pp. 201–222.Google Scholar

Copyright information

© Deutsche Gesellschaft für Säugetierkunde 2011

Authors and Affiliations

  • Joerns Fickel
    • 1
    Email author
  • Oleg A. Bubliy
    • 1
  • Julia Brand
    • 1
  • Kathrin Mayer
    • 2
  • Marco Heurich
    • 2
  1. 1.Dept. Evolutionary GeneticsLeibniz-Institute for Zoo and Wildlife ResearchBerlinGermany
  2. 2.Dept. Research and DocumentationBavarian Forest National ParkGrafenauGermany

Personalised recommendations