Effects of different environmental and sampling variables on the genotyping success in field-collected scat samples: a brown bear case study

Authors

  • Tomaž Skrbinšek

DOI:

https://doi.org/10.14720/abs.63.2.15940

Keywords:

genetics, genotyping success, molecular ecology, noninvasive sampling, scat sampling, Ursus arctos

Abstract

The paper investigates how different field conditions and sample characteristics influence genotyping success in field-collected brown bear scat samples. Genotyping performance of 413 samples collected in a pilot study in southern Slovenia was evaluated, andstatistical modelling was used to control confounding between pre- dictor variables and to quantify their specific effects ongenotyping success. The best predictors of genotyping success were subjectively estimated scat age, sampling month, and contents of ascat. Even when the other confounded variables were controlled for, genotyping success dropped rapidly with the age estimate, from 89% (82-94%) for 0-day scats to 33% (19-52%) for scats estimated to be 5 days old. Sampling month was also an important predictor, and samples collected during the bear hyperphagia period in late summer/autumn performed considerably better (90%,78-96%) than the samples collected in spring / early summer (66%, 57-74%). This effect was stronger for fresh than for older samples. Effects of different food types were also considerable, but less important for practical use. Since noninvasive genetic sampling already became the key method for surveying wild populations of many species, efficiency of studies is becoming increasingly important. Understanding the effect of the month of sampling allows the field season to be timed for maximum genotyping success, while subjective scat age provides a useful metric that indicates a sample’s viability for genotyping, allowing for prioritization of samples and culling of non-viable samples before resources are wasted for their analysis. This provides higher useful data yields per invested resources and may ultimately lead to better study results.

References

Akaike, H., 1973. Information theory and an extension of the maximum likelihood principle. Pages 267–281 in B. N. Petrov and F. Caski, editors. Proceedings of the Second International Symposium on Information Theory. Akademiai Kiado, Budapest, Hungary.

Andrews, K.R., De Barba, M., Russello, M.A., Waits, L.P., 2018. Advances in using non-invasive, archival, and environmental samples for population genomic studies. Springer Link, 1–37. doi: 10.1007/13836_2018_45 DOI: https://doi.org/10.1007/13836_2018_45

Bartoń, K., 2020. MuMIn: Multi-model inference. Available from https://cran.r-project.org/ package=MuMIn.

Brinkman, T.J., Schwartz, M.K., Person, D.K., Pilgrim, K.L., Hundertmark, K.J., 2010. Effects of time and rainfall on PCR success using DNA extracted from deer fecal pellets. Conservation Genetics, 11, 1547–1552. DOI: https://doi.org/10.1007/s10592-009-9928-7

Broquet, T., Ménard, N., Petit, E., 2007. Noninvasive population genetics: A review of sample source, diet, fragment length and microsatellite motif effects on amplification success and genotyping error rates. Conservation Genetics, 8, 249–260. DOI: https://doi.org/10.1007/s10592-006-9146-5

Burnham, K.P., Anderson, D.R., 2002. Model selection and multimodel inference. New York.

Carroll, E.L., Bruford, M.W., DeWoody, J.A., Leroy, G., Strand, A., Waits, L., Wang, J., 2018. Genetic and genomic monitoring with minimally invasive sampling methods. Evolutionary Applications, 11, 1094–1119. DOI: https://doi.org/10.1111/eva.12600

De Barba, M., Miquel, C., Lobréaux, S., Quenette, P.Y., Swenson, J.E., Taberlet, P., 2017. High- throughput microsatellite genotyping in ecology: improved accuracy, efficiency, standardization and success with low-quantity and degraded DNA. Molecular Ecology Resources, 17, 492–507. DOI: https://doi.org/10.1111/1755-0998.12594

De Barba, M., Waits, L.P., 2010. Multiplex pre-amplification for noninvasive genetic sampling: Is the extra effort worth it? Molecular Ecology Resources, 10, 659–665. DOI: https://doi.org/10.1111/j.1755-0998.2009.02818.x

Demay, S.M., Becker, P.A., Eidson, C.A., Rachlow, J.L., Johnson, T.R., Waits, L.P., 2013. Evaluating DNA degradation rates in faecal pellets of the endangered pygmy rabbit. Molecular Ecology Re- sources, 13, 654–662. DOI: https://doi.org/10.1111/1755-0998.12104

De Barba, M., Waits, P.L., Genovesi, P., Randi, E., Chirichella, R., Cetto, E., 2010. Comparing opportu- nistic and systematic sampling methods for non-invasive genetic monitoring of a small translocated brown bear population. Journal of Applied Ecology, 47, 172–181. DOI: https://doi.org/10.1111/j.1365-2664.2009.01752.x

DeWoody, A.J., 2005. Molecular approaches to the study of parentage, relatedness, and fitness: practical applications for wild animals. Journal of Wildlife Management, 69, 1400–1418. DOI: https://doi.org/10.2193/0022-541X(2005)69[1400:MATTSO]2.0.CO;2

DeYoung, R.W., Honeycutt, R.L., 2005. The molecular toolbox: genetic techniques in wildlife ecology and management. Journal of Wildlife Management, 69, 1362–1384. DOI: https://doi.org/10.2193/0022-541X(2005)69[1362:TMTGTI]2.0.CO;2

Gagneux, P., Woodruff, D.S., Boesch, C., 1997. Furtive mating in female chimpanzees. Nature, 387, 358–359. DOI: https://doi.org/10.1038/387358a0

King, S.R.B., Schoenecker, K.A., Fike, J.A., Oyler-McCance, S.J., 2018. Long-term persistence of horse fecal DNA in the environment makes equids particularly good candidates for noninvasive sampling. Ecology and Evolution, 8, 4053–4064. DOI: https://doi.org/10.1002/ece3.3956

Kopatz, A., Kleven, O., Flagstad, Ø., 2020. Seasonal variation of success in DNA- extraction from brown bear fecal samples. Trondheim. Available from https://brage.nina.no/nina-xmlui/bitstream/ handle/11250/2640529/1775.pdf?sequence=1.

López-Alfaro, C., Robbins, C.T., Zedrosser, A., Nielsen, S.E., 2013. Energetics of hibernation and reproductive trade-offs in brown bears. Ecological Modelling, 270, 1–10. DOI: https://doi.org/10.1016/j.ecolmodel.2013.09.002

Lüdecke, D., 2018. ggeffects: Tidy data frames of marginal effects from regression models. Available from https://doi.org/10.21105/joss.00772 (accessed November 19, 2020). DOI: https://doi.org/10.21105/joss.00772

Miquel, C., Bellemain, E., Poillot, C., Bessière, J., Durand, A., Taberlet P., 2006a. Quality indexes to assess the reliability of genotypes in studies using noninvasive sampling and multiple-tube approach. Molecular Ecology Notes, 6, 985–988.

Miquel C., Bellemain E., Poillot J., Bessiere J., Durand A., Taberlet P., 2006b. Quality indexes to as- sess the reliability of genotypes in studies using noninvasive sampling and muliple-tube approach. Molecular Ecology Notes, 6, 985–988. DOI: https://doi.org/10.1111/j.1471-8286.2006.01413.x

Mumma, M.A., Adams, J.R., Zieminski, C., Fuller, T.K., Mahoney, S.P., Waits, L.P., 2016. A comparison of morphological and molecular diet analyses of predator scats. Journal of Mammalogy, 97, 112–120. DOI: https://doi.org/10.1093/jmammal/gyv160

Murphy, M.A., Kendall, K.C., Robinson, A., Waits, L.P., 2007. The impact of time and field conditions on brown bear (Ursus arctos) faecal DNA amplification. Conservation Genetics, 8, 1219–1224. DOI: https://doi.org/10.1007/s10592-006-9264-0

Murphy, M.A., Waits, L.P., Kendall, K.C., 2003. The influence of diet on faecal DNA amplification and sex identification in brown bears (Ursus arctos). Molecular Ecology, 12, 2261–2265. DOI: https://doi.org/10.1046/j.1365-294X.2003.01863.x

Panasci, M., Ballard, W.B., Breck, S., Rodriguez, D., Densmore, L.D., Wester, D.B., Baker, R.J., 2011. Evaluation of fecal DNA preservation techniques and effects of sample age and diet on genotyping success. Journal of Wildlife Management, 75, 1616–1624. DOI: https://doi.org/10.1002/jwmg.221

Piggott, M.P., 2004. Effect of sample age and season of collection on the reliability of microsatellite genotyping of faecal DNA. Wildlife Research, 31, 485–493. DOI: https://doi.org/10.1071/WR03096

Piggott, M.P., Taylor, A.C., 2003. Extensive evaluation of faecal preservation and DNA extraction methods in Australian native and introduced species. Australian Journal of Zoology, 51, 341–355. DOI: https://doi.org/10.1071/ZO03012

R Core Team., 2020. R: A language and environment for statistical computing. R Foundation for Sta- tistical Computing, Vienna, Austria.

RStudio Team, 2020. RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. url: http:// www.rstudio.com/.

Santini, A., Lucchini, V., Fabbri, E., Randi, E., 2007. Ageing and environmental factors affect PCR success in wolf (Canis lupus) excremental DNA samples. Molecular Ecology Notes, 7, 955–961. DOI: https://doi.org/10.1111/j.1471-8286.2007.01829.x

Skrbinšek, T., Jelenčič, M., Waits, L., Kos, I., Trontelj, P., 2010. Highly efficient multiplex PCR of noninvasive DNA does not require pre-amplification. Molecular Ecology Resources, 10, 495–501. DOI: https://doi.org/10.1111/j.1755-0998.2009.02780.x

Skrbinšek, T., Luštrik, R., Majić-Skrbinšek, A., Potočnik, H., Kljun, F., Jelenčič, M., Kos, I., Trontelj P., 2019. From science to practice : genetic estimate of brown bear population size in Slovenia and how it influenced bear management. European Journal of Wildlife Research, 65, 1–15. DOI: https://doi.org/10.1007/s10344-019-1265-7

Taberlet, P., Griffin, S., Goossens, B., Questiau, S., Manceau, V., Escaravage, N., Waits, L. P., Bouvet, J., 1996. Reliable genotyping of samples with very low DNA quantities using PCR. Nucleic Acids Research, 24, 3189–3194. DOI: https://doi.org/10.1093/nar/24.16.3189

Taberlet, P., Waits, L. P., Luikart, G., 1999. Noninvasive genetic sampling: look before you leap. Trends in Ecology & Evolution, 14, 323–327. DOI: https://doi.org/10.1016/S0169-5347(99)01637-7

Waits, L.P., Paetkau, D.W., 2005. Noninvasive genetic sampling tools for wildlife biologists: a review of applications and recommendations for accurate data collection. Journal of Wildlife Management, 69, 1419–1433. DOI: https://doi.org/10.2193/0022-541X(2005)69[1419:NGSTFW]2.0.CO;2

Wickham, H., 2016. ggplot2: elegant graphics for data analysis. Springer. DOI: https://doi.org/10.1007/978-3-319-24277-4

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Published

01.12.2020

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Section

Original Research Paper

How to Cite

Skrbinšek, T. (2020). Effects of different environmental and sampling variables on the genotyping success in field-collected scat samples: a brown bear case study. Acta Biologica Slovenica, 63(2), 89-98. https://doi.org/10.14720/abs.63.2.15940